AML Mini-Challenge - Credit Card Affinity Modelling

import random
from collections import OrderedDict
from datetime import datetime

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

sns.set_theme()
# plt.style.use('seaborn-white')
# plt.style.use('ggplot')

data_reduction = OrderedDict()

SEED = 1337

def seed_everything(seed):
    np.random.seed(seed)
    random.seed(seed)
    
seed_everything(SEED)

1 Data Import & Wrangling

1.1 Helper Functions

def remap_values(df, column, mapping):
    # assert that all values in the column are in the mapping except for NaN
    assert df[column].dropna().isin(mapping.keys()).all()

    df[column] = df[column].map(mapping, na_action="ignore")
    return df


def map_empty_to_nan(df, column):
    if df[column].dtype != "object":
        return df

    df[column] = df[column].replace(r"^\s*$", np.nan, regex=True)
    return df


def read_csv(file_path, sep=";", dtypes=None):
    df = pd.read_csv(file_path, sep=sep, dtype=dtypes)

    for col in df.columns:
        df = map_empty_to_nan(df, col)

    return df

def plot_categorical_variables(df, categorical_columns, fill_na_value="NA", rotate_x=False):
    """
    Plots count plots for categorical variables in a DataFrame, filling NA values with a specified string.

    Parameters:
    - df: pandas.DataFrame containing the data.
    - categorical_vars: list of strings, names of the categorical variables in df to plot.
    - fill_na_value: string, the value to use for filling NA values in the categorical variables.
    """
    # Fill NA values in the specified categorical variables
    for var in categorical_columns:
        if df[var].isna().any():
            df[var] = df[var].fillna(fill_na_value)

    total = float(len(df))
    fig, axes = plt.subplots(
        nrows=len(categorical_columns), figsize=(8, len(categorical_columns) * 5)
    )

    if len(categorical_columns) == 1:  # If there's only one categorical variable, wrap axes in a list
        axes = [axes]

    for i, var in enumerate(categorical_columns):
        ax = sns.countplot(
            x=var, data=df, ax=axes[i], order=df[var].value_counts().index
        )

        axes[i].set_title(f"Distribution of {var}")
        axes[i].set_ylabel("Count")
        axes[i].set_xlabel(var)

        # if the number is more than 5 rotate the x labels or if specified
        if len(df[var].value_counts()) > 5 or rotate_x:
            ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")

        for p in ax.patches:
            height = p.get_height()
            ax.text(
                p.get_x() + p.get_width() / 2.0,
                height + 3,
                "{:1.2f}%".format((height / total) * 100),
                ha="center",
            )

    plt.tight_layout()
    plt.show()


def plot_numerical_distributions(df, numerical_columns, kde=True, bins=30):
    """
    Plots the distribution of all numerical variables in a DataFrame.

    Parameters:
    - df: pandas.DataFrame containing the data.
    """

    # Determine the number of rows needed for subplots based on the number of numerical variables
    nrows = len(numerical_columns)

    fig, axes = plt.subplots(nrows=nrows, ncols=1, figsize=(8, 5 * nrows))

    # If there's only one numerical variable, wrap axes in a list
    if nrows == 1:  
        axes = [axes]

    for i, var in enumerate(numerical_columns):
        sns.histplot(df[var], ax=axes[i], kde=kde, bins=bins)
        axes[i].set_title(f"Distribution of {var}")
        axes[i].set_xlabel(var)
        axes[i].set_ylabel("Frequency")

    plt.tight_layout()
    plt.show()


def plot_date_monthly_counts(df, date_column, title):
    """
    Plots the monthly counts of a date column in a DataFrame.

    Parameters:
    - df: pandas.DataFrame containing the data.
    - date_column: string, name of the date column in df to plot.
    - title: string, title of the plot.
    """
    df[date_column] = pd.to_datetime(df[date_column])
    df["month"] = df[date_column].dt.to_period("M")

    monthly_counts = df["month"].value_counts().sort_index()
    monthly_counts.plot(kind="bar")
    plt.title(title)
    plt.xlabel("Month")
    plt.ylabel("Count")
    plt.show()


def add_percentage_labels(ax, hue_order):
    for p in ax.patches:
        height = p.get_height()
        width = p.get_width()
        x = p.get_x()
        y = p.get_y()
        label_text = f"{height:.1f}%"
        label_x = x + width / 2
        label_y = y + height / 2
        ax.text(
            label_x,
            label_y,
            label_text,
            ha="center",
            va="center",
            fontsize=9,
            color="white",
            weight="bold"
        )

1.2 Entities

The following section will describe the individual entities in the dataset.

Accordingly there is some simple remapping done to allow for easier understanding of the data. This can include translating the values from Czech to English or remapping the values to a more understandable format.

See code comments for more details of how the data is transformed.

1.2.1 Accounts

accounts_df = read_csv("data/account.csv")

# translated frequency from Czech to English
# according to https://sorry.vse.cz/~berka/challenge/PAST/index.html
accounts_df = remap_values(
    accounts_df,
    "frequency",
    {
        "POPLATEK MESICNE": "MONTHLY_ISSUANCE",
        "POPLATEK TYDNE": "WEEKLY_ISSUANCE",
        "POPLATEK PO OBRATU": "ISSUANCE_AFTER_TRANSACTION",
    },
)

accounts_df["date"] = pd.to_datetime(accounts_df["date"], format="%y%m%d")

accounts_df.rename(
    columns={"date": "account_created", "frequency": "account_frequency"}, inplace=True
)

data_reduction["Total number of accounts"] = len(accounts_df)
accounts_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4500 entries, 0 to 4499
Data columns (total 4 columns):
 #   Column             Non-Null Count  Dtype         
---  ------             --------------  -----         
 0   account_id         4500 non-null   int64         
 1   district_id        4500 non-null   int64         
 2   account_frequency  4500 non-null   object        
 3   account_created    4500 non-null   datetime64[ns]
dtypes: datetime64[ns](1), int64(2), object(1)
memory usage: 140.8+ KB

accounts_df.head()

	account_id	district_id	account_frequency	account_created
0	576	55	MONTHLY_ISSUANCE	1993-01-01
1	3818	74	MONTHLY_ISSUANCE	1993-01-01
2	704	55	MONTHLY_ISSUANCE	1993-01-01
3	2378	16	MONTHLY_ISSUANCE	1993-01-01
4	2632	24	MONTHLY_ISSUANCE	1993-01-02

accounts_df.nunique()

account_id           4500
district_id            77
account_frequency       3
account_created      1535
dtype: int64

plot_categorical_variables(accounts_df, ["account_frequency"])

plot_numerical_distributions(accounts_df, ["account_created"])

1.2.2 Clients

clients_df = read_csv("data/client.csv")


def parse_birth_number(birth_number):
    birth_number_str = str(birth_number)

    # extract year, month, and day from birth number from string
    # according to https://sorry.vse.cz/~berka/challenge/PAST/index.html
    year = int(birth_number_str[:2])
    month = int(birth_number_str[2:4])
    day = int(birth_number_str[4:6])

    # determine sex based on month and adjust month for female clients
    # according to https://sorry.vse.cz/~berka/challenge/PAST/index.html
    if month > 50:
        sex = "Female"
        month -= 50
    else:
        sex = "Male"

    # quick validation
    assert 1 <= month <= 12
    assert 1 <= day <= 31
    assert 0 <= year <= 99

    if month in [4, 6, 9, 11]:
        assert 1 <= day <= 30
    elif month == 2:
        assert 1 <= day <= 29
    else:
        assert 1 <= day <= 31

    # assuming all dates are in the 1900s
    birth_date = datetime(1900 + year, month, day)
    return pd.Series([sex, birth_date])


clients_df[["sex", "birth_date"]] = clients_df["birth_number"].apply(parse_birth_number)

# calculate 'age' assuming the reference year is 1999
clients_df["age"] = clients_df["birth_date"].apply(lambda x: 1999 - x.year)

# drop 'birth_number' column as it is no longer needed
clients_df = clients_df.drop(columns=["birth_number"])

clients_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5369 entries, 0 to 5368
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype         
---  ------       --------------  -----         
 0   client_id    5369 non-null   int64         
 1   district_id  5369 non-null   int64         
 2   sex          5369 non-null   object        
 3   birth_date   5369 non-null   datetime64[ns]
 4   age          5369 non-null   int64         
dtypes: datetime64[ns](1), int64(3), object(1)
memory usage: 209.9+ KB

clients_df.head()

	client_id	district_id	sex	birth_date	age
0	1	18	Female	1970-12-13	29
1	2	1	Male	1945-02-04	54
2	3	1	Female	1940-10-09	59
3	4	5	Male	1956-12-01	43
4	5	5	Female	1960-07-03	39

clients_df.describe()

	client_id	district_id	birth_date	age
count	5369.000000	5369.000000	5369	5369.000000
mean	3359.011920	37.310114	1953-09-12 09:32:21.143602176	45.801639
min	1.000000	1.000000	1911-08-20 00:00:00	12.000000
25%	1418.000000	14.000000	1940-11-25 00:00:00	31.000000
50%	2839.000000	38.000000	1954-05-06 00:00:00	45.000000
75%	4257.000000	60.000000	1968-06-09 00:00:00	59.000000
max	13998.000000	77.000000	1987-09-27 00:00:00	88.000000
std	2832.911984	25.043690	NaN	17.282283

# todo validate bins
plot_numerical_distributions(clients_df, ["birth_date", "age"], bins=40)

Here we can see the distribution of of birth dates and the age of the clients. We see that the majority of clients are between 20 and 60 years old. There are a few noticeable drops at around age 30, 50 and 70 years, which is not really of any concern.

We also notice that there are a few underage clients.

1.2.3 Dispositions

dispositions_df = read_csv("data/disp.csv")
dispositions_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5369 entries, 0 to 5368
Data columns (total 4 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   disp_id     5369 non-null   int64 
 1   client_id   5369 non-null   int64 
 2   account_id  5369 non-null   int64 
 3   type        5369 non-null   object
dtypes: int64(3), object(1)
memory usage: 167.9+ KB

dispositions_df.head()

	disp_id	client_id	account_id	type
0	1	1	1	OWNER
1	2	2	2	OWNER
2	3	3	2	DISPONENT
3	4	4	3	OWNER
4	5	5	3	DISPONENT

dispositions_df.describe()

	disp_id	client_id	account_id
count	5369.000000	5369.000000	5369.000000
mean	3337.097970	3359.011920	2767.496927
std	2770.418826	2832.911984	2307.843630
min	1.000000	1.000000	1.000000
25%	1418.000000	1418.000000	1178.000000
50%	2839.000000	2839.000000	2349.000000
75%	4257.000000	4257.000000	3526.000000
max	13690.000000	13998.000000	11382.000000

plot_categorical_variables(dispositions_df, ["type"])

The plot above shows the distribution of account types. We can see that roughly 4 out 5 accounts are categorized as “OWNER” accounts.

As the goal of this model is to address accounts and not client directly we will focus on the clients which own an account and focus solely on them.

dispositions_df = dispositions_df[dispositions_df["type"] == "OWNER"]

1.2.4 Orders

orders_df = read_csv("data/order.csv")

# Translated from Czech to English
# according to https://sorry.vse.cz/~berka/challenge/PAST/index.html
orders_df = remap_values(
    orders_df,
    "k_symbol",
    {
        "POJISTNE": "Insurance_Payment",
        "SIPO": "Household",
        "LEASING": "Leasing",
        "UVER": "Loan_Payment",
    },
)

orders_df["account_to"] = orders_df["account_to"].astype("category")

orders_df = orders_df.rename(columns={"amount": "debited_amount"})

orders_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6471 entries, 0 to 6470
Data columns (total 6 columns):
 #   Column          Non-Null Count  Dtype   
---  ------          --------------  -----   
 0   order_id        6471 non-null   int64   
 1   account_id      6471 non-null   int64   
 2   bank_to         6471 non-null   object  
 3   account_to      6471 non-null   category
 4   debited_amount  6471 non-null   float64 
 5   k_symbol        5092 non-null   object  
dtypes: category(1), float64(1), int64(2), object(2)
memory usage: 573.9+ KB

orders_df.head()

	order_id	account_id	bank_to	account_to	debited_amount	k_symbol
0	29401	1	YZ	87144583	2452.0	Household
1	29402	2	ST	89597016	3372.7	Loan_Payment
2	29403	2	QR	13943797	7266.0	Household
3	29404	3	WX	83084338	1135.0	Household
4	29405	3	CD	24485939	327.0	NaN

orders_df.describe()

	order_id	account_id	debited_amount
count	6471.000000	6471.000000	6471.000000
mean	33778.197497	2962.302890	3280.635698
std	3737.681949	2518.503228	2714.475335
min	29401.000000	1.000000	1.000000
25%	31187.500000	1223.000000	1241.500000
50%	32988.000000	2433.000000	2596.000000
75%	34785.500000	3645.500000	4613.500000
max	46338.000000	11362.000000	14882.000000

orders_df.nunique()

order_id          6471
account_id        3758
bank_to             13
account_to        6446
debited_amount    4412
k_symbol             4
dtype: int64

There appear to be as many order ids as there are rows. This implies that each order is unique which makes sense.

plot_categorical_variables(orders_df, ["k_symbol", "bank_to"])

/tmp/ipykernel_1878/3089943057.py:34: UserWarning:

set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.

Taking a closer look at the distribution of type of order (k_symbol) and receiving bank (bank_to) we can see that the majority of orders are Household orders and NA.

We also see that the receiving bank is somewhat equally distributed, meaning that the orders are not concentrated on a single bank.

# todo not sure what debited_amount is
plot_numerical_distributions(orders_df, ["debited_amount"])

1.2.5 Transactions

# column 8 is the 'bank' column which contains NaNs and must be read as string
transactions_df = read_csv("data/trans.csv", dtypes={8: str})

transactions_df["date"] = pd.to_datetime(transactions_df["date"], format="%y%m%d")

# Translated type, operations and characteristics from Czech to English
# according to https://sorry.vse.cz/~berka/challenge/PAST/index.html
transactions_df = remap_values(
    transactions_df,
    "type",
    {
        "VYBER": "Withdrawal",  # Also withdrawal as it is against the documentation present in the dataset
        "PRIJEM": "Credit",
        "VYDAJ": "Withdrawal",
    },
)

transactions_df = remap_values(
    transactions_df,
    "operation",
    {
        "VYBER KARTOU": "Credit Card Withdrawal",
        "VKLAD": "Credit in Cash",
        "PREVOD Z UCTU": "Collection from Another Bank",
        "VYBER": "Withdrawal in Cash",
        "PREVOD NA UCET": "Remittance to Another Bank",
    },
)

transactions_df = remap_values(
    transactions_df,
    "k_symbol",
    {
        "POJISTNE": "Insurance Payment",
        "SLUZBY": "Payment on Statement",
        "UROK": "Interest Credited",
        "SANKC. UROK": "Sanction Interest",
        "SIPO": "Household",
        "DUCHOD": "Old-age Pension",
        "UVER": "Loan Payment",
    },
)

# set the amount to negative for withdrawals and positive for credits
transactions_df["amount"] = np.where(
    transactions_df["type"] == "Credit",
    transactions_df["amount"],
    -transactions_df["amount"],
)

transactions_df.rename(columns={"type": "transaction_type"}, inplace=True)

transactions_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1056320 entries, 0 to 1056319
Data columns (total 10 columns):
 #   Column            Non-Null Count    Dtype         
---  ------            --------------    -----         
 0   trans_id          1056320 non-null  int64         
 1   account_id        1056320 non-null  int64         
 2   date              1056320 non-null  datetime64[ns]
 3   transaction_type  1056320 non-null  object        
 4   operation         873206 non-null   object        
 5   amount            1056320 non-null  float64       
 6   balance           1056320 non-null  float64       
 7   k_symbol          521006 non-null   object        
 8   bank              273508 non-null   object        
 9   account           295389 non-null   float64       
dtypes: datetime64[ns](1), float64(3), int64(2), object(4)
memory usage: 80.6+ MB

transactions_df.head()

	trans_id	account_id	date	transaction_type	operation	amount	balance	k_symbol	bank	account
0	695247	2378	1993-01-01	Credit	Credit in Cash	700.0	700.0	NaN	NaN	NaN
1	171812	576	1993-01-01	Credit	Credit in Cash	900.0	900.0	NaN	NaN	NaN
2	207264	704	1993-01-01	Credit	Credit in Cash	1000.0	1000.0	NaN	NaN	NaN
3	1117247	3818	1993-01-01	Credit	Credit in Cash	600.0	600.0	NaN	NaN	NaN
4	579373	1972	1993-01-02	Credit	Credit in Cash	400.0	400.0	NaN	NaN	NaN

transactions_df.describe()

	trans_id	account_id	date	amount	balance	account
count	1.056320e+06	1.056320e+06	1056320	1.056320e+06	1.056320e+06	2.953890e+05
mean	1.335311e+06	2.936867e+03	1997-01-04 07:29:27.037261952	1.866397e+02	3.851833e+04	4.567092e+07
min	1.000000e+00	1.000000e+00	1993-01-01 00:00:00	-8.740000e+04	-4.112570e+04	0.000000e+00
25%	4.302628e+05	1.204000e+03	1996-01-16 00:00:00	-3.019000e+03	2.240250e+04	1.782858e+07
50%	8.585065e+05	2.434000e+03	1997-04-10 00:00:00	-1.460000e+01	3.314340e+04	4.575095e+07
75%	2.060979e+06	3.660000e+03	1998-02-28 00:00:00	2.000000e+02	4.960362e+04	7.201341e+07
max	3.682987e+06	1.138200e+04	1998-12-31 00:00:00	7.481200e+04	2.096370e+05	9.999420e+07
std	1.227487e+06	2.477345e+03	NaN	1.121353e+04	2.211787e+04	3.066340e+07

plot_categorical_variables(
    transactions_df, ["transaction_type", "operation", "k_symbol"]
)

/tmp/ipykernel_1878/3089943057.py:34: UserWarning:

set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.

/tmp/ipykernel_1878/3089943057.py:34: UserWarning:

set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.

plot_numerical_distributions(transactions_df, ["date", "amount", "balance"])

Looking at the distributions of the transaction table we can see that the count of transactions per year increase over time. So we can conclude that the bank has a growing client base. It may however also show that there are more transactions per client.

However, the other plots are not very useful. For one the transaction amount seems to be very sparse, ranging from values between -80000 and 80000.

The balance distribution also showcases that there are accounts with a negative balance after a transaction, which would only make sense if debt is also included in this value.

According to description of the field balance: “balance after transaction”

1.2.5.1 {{{{POTENTIAL ADD A PLOT SHOWCASING CLIENT BASE GROWTH}}}}

1.2.5.2 Transaction Amounts and Counts by Month

# Getting a list of unique years from the dataset
transactions_df["year"] = transactions_df["date"].dt.year
transactions_df["month"] = transactions_df["date"].dt.month

months = [
    "Jan",
    "Feb",
    "Mar",
    "Apr",
    "May",
    "Jun",
    "Jul",
    "Aug",
    "Sep",
    "Oct",
    "Nov",
    "Dec",
]
years = sorted(transactions_df["year"].unique())

fig, axs = plt.subplots(
    len(years) * 2,
    1,
    figsize=(8, 6 * len(years)),
    sharex=True,
    gridspec_kw={"height_ratios": [3, 1] * len(years)},
)

for i, year in enumerate(years):
    # filter transactions for the current year
    yearly_transactions = transactions_df[transactions_df["year"] == year]

    # preparing data for the box plot: a list of amounts for each month for the current year
    amounts_per_month_yearly = [
        yearly_transactions[yearly_transactions["month"] == month]["amount"]
        for month in range(1, 13)
    ]

    # preparing data for the bar chart for the current year
    monthly_summary_yearly = (
        yearly_transactions.groupby("month")
        .agg(TotalAmount=("amount", "sum"), TransactionCount=("amount", "count"))
        .reset_index()
    )

    # box plot for transaction amounts by month for the current year
    axs[i * 2].boxplot(amounts_per_month_yearly, patch_artist=True)
    axs[i * 2].set_title(f"Transaction Amounts Per Month in {year} (Box Plot)")
    axs[i * 2].set_yscale("symlog")
    axs[i * 2].set_ylabel("Transaction Amounts (log scale)")
    axs[i * 2].grid(True, which="both")

    # Bar chart for transaction count by month for the current year
    axs[i * 2 + 1].bar(
        monthly_summary_yearly["month"],
        monthly_summary_yearly["TransactionCount"],
        color="tab:red",
        alpha=0.6,
    )
    axs[i * 2 + 1].set_ylabel("Transaction Count")
    axs[i * 2 + 1].grid(True, which="both")

# Setting x-ticks and labels for the last bar chart (shared x-axis for all)
axs[-1].set_xticks(range(1, 13))
axs[-1].set_xticklabels(months)
axs[-1].set_xlabel("Month")

plt.tight_layout()
plt.show()

## THIS IS DEAD todo validate quickly
fig, axs = plt.subplots(
    2,
    len(years),
    figsize=(8 * len(years) / 2, 7),
    sharey="row",
    gridspec_kw={"height_ratios": [3, 1]},
)

for i, year in enumerate(years):
    # Filter transactions for the current year
    yearly_transactions = transactions_df[transactions_df["year"] == year]

    # Preparing data for the box plot: a list of amounts for each month for the current year
    amounts_per_month_yearly = [
        yearly_transactions[yearly_transactions["month"] == month]["amount"]
        for month in range(1, 13)
    ]

    # Preparing data for the bar chart for the current year
    monthly_summary_yearly = (
        yearly_transactions.groupby("month")
        .agg(TotalAmount=("amount", "sum"), TransactionCount=("amount", "count"))
        .reset_index()
    )

    # Selecting the appropriate axes for multiple or single year scenarios
    ax_box = axs[0, i] if len(years) > 1 else axs[0]
    ax_bar = axs[1, i] if len(years) > 1 else axs[1]

    ax_box.boxplot(amounts_per_month_yearly, patch_artist=True)
    ax_box.set_title(f"{year} (Box Plot)")
    ax_box.set_yscale("symlog")
    ax_box.set_ylabel("Transaction Amounts (log scale)")
    ax_box.grid(True, which="both")

    ax_bar.bar(
        monthly_summary_yearly["month"],
        monthly_summary_yearly["TransactionCount"],
        color="tab:red",
        alpha=0.6,
    )
    ax_bar.set_ylabel("Transaction Count")
    ax_bar.grid(True, which="both")

    # Setting common x-ticks and labels for all axes
    ax_bar.set_xticks(range(1, 13))
    ax_bar.set_xticklabels(months)

fig.text(0.5, 0.04, "Month", ha="center")
plt.tight_layout()
# plt.show()

1.2.5.3 Negative Balances

negative_balances = transactions_df[transactions_df["balance"] < 0]
plot_numerical_distributions(negative_balances, ["balance", "amount"])
print(f"Number of transactions with negative balance: {len(negative_balances)}")

Number of transactions with negative balance: 2999

There appear to be 2999 transactions which have a negative balance, therefore after the transaction the account balance was negative. This implies that these accounts are in some kind of debt.

1.2.6 Loans

loans_df = read_csv("data/loan.csv")

loans_df["date"] = pd.to_datetime(loans_df["date"], format="%y%m%d")

loans_df["status"] = loans_df["status"].map(
    {
        "A": "Contract finished, no problems",
        "B": "Contract finished, loan not paid",
        "C": "Contract running, OK thus-far",
        "D": "Contract running, client in debt",
    }
)

loans_df.rename(
    columns={
        "date": "granted_date",
        "amount": "amount",
        "duration": "duration",
        "payments": "monthly_payments",
        "status": "status",
    },
    inplace=True,
)

loans_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 682 entries, 0 to 681
Data columns (total 7 columns):
 #   Column            Non-Null Count  Dtype         
---  ------            --------------  -----         
 0   loan_id           682 non-null    int64         
 1   account_id        682 non-null    int64         
 2   granted_date      682 non-null    datetime64[ns]
 3   amount            682 non-null    int64         
 4   duration          682 non-null    int64         
 5   monthly_payments  682 non-null    float64       
 6   status            682 non-null    object        
dtypes: datetime64[ns](1), float64(1), int64(4), object(1)
memory usage: 37.4+ KB

loans_df.head()

	loan_id	account_id	granted_date	amount	duration	monthly_payments	status
0	5314	1787	1993-07-05	96396	12	8033.0	Contract finished, loan not paid
1	5316	1801	1993-07-11	165960	36	4610.0	Contract finished, no problems
2	6863	9188	1993-07-28	127080	60	2118.0	Contract finished, no problems
3	5325	1843	1993-08-03	105804	36	2939.0	Contract finished, no problems
4	7240	11013	1993-09-06	274740	60	4579.0	Contract finished, no problems

loans_df.describe()

	loan_id	account_id	granted_date	amount	duration	monthly_payments
count	682.000000	682.000000	682	682.000000	682.000000	682.000000
mean	6172.466276	5824.162757	1996-09-29 05:35:43.108504448	151410.175953	36.492669	4190.664223
min	4959.000000	2.000000	1993-07-05 00:00:00	4980.000000	12.000000	304.000000
25%	5577.500000	2967.000000	1995-07-04 12:00:00	66732.000000	24.000000	2477.000000
50%	6176.500000	5738.500000	1997-02-06 12:00:00	116928.000000	36.000000	3934.000000
75%	6752.500000	8686.000000	1997-12-12 12:00:00	210654.000000	48.000000	5813.500000
max	7308.000000	11362.000000	1998-12-08 00:00:00	590820.000000	60.000000	9910.000000
std	682.579279	3283.512681	NaN	113372.406310	17.075219	2215.830344

loans_df.nunique()

loan_id             682
account_id          682
granted_date        559
amount              645
duration              5
monthly_payments    577
status                4
dtype: int64

It seems as if one account can have at max one loan.

plot_categorical_variables(loans_df, ["duration", "status"], rotate_x=True)

/tmp/ipykernel_1878/3089943057.py:34: UserWarning:

set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.

/tmp/ipykernel_1878/3089943057.py:34: UserWarning:

set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.

The distribution of durations seems to be even. Most of the loans are still running and the majority of loans are in good standing. Around 200 loans are finished without problems. Around 11% of loans are in a potentially problematic state.

plot_numerical_distributions(loans_df, ["granted_date"])

The distribution of granted dates shows a steady increase with a drop at 1995-1996 before reaching its peak at 1998.

1.2.7 Credit Cards

cards_df = read_csv("data/card.csv")

cards_df["issued"] = pd.to_datetime(
    cards_df["issued"], format="%y%m%d %H:%M:%S"
).dt.date

cards_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 892 entries, 0 to 891
Data columns (total 4 columns):
 #   Column   Non-Null Count  Dtype 
---  ------   --------------  ----- 
 0   card_id  892 non-null    int64 
 1   disp_id  892 non-null    int64 
 2   type     892 non-null    object
 3   issued   892 non-null    object
dtypes: int64(2), object(2)
memory usage: 28.0+ KB

cards_df.head()

	card_id	disp_id	type	issued
0	1005	9285	classic	1993-11-07
1	104	588	classic	1994-01-19
2	747	4915	classic	1994-02-05
3	70	439	classic	1994-02-08
4	577	3687	classic	1994-02-15

cards_df.describe()

	card_id	disp_id
count	892.000000	892.000000
mean	480.855381	3511.862108
std	306.933982	2984.373626
min	1.000000	9.000000
25%	229.750000	1387.000000
50%	456.500000	2938.500000
75%	684.250000	4459.500000
max	1247.000000	13660.000000

plot_categorical_variables(cards_df, ["type"])

We can see that a majority of the credit cards issued are classic. A small fraction of 16.26% of the credit cards are junior cards and 9.87% are gold cards.

# todo look at this.
plot_numerical_distributions(cards_df, ["issued"])

1.2.8 Demographic data

districts_df = read_csv("data/district.csv")

# rename columns
# according to https://sorry.vse.cz/~berka/challenge/PAST/index.html
districts_df.rename(
    columns={
        "A1": "district_id",
        "A2": "district_name",
        "A3": "region",
        "A4": "inhabitants",
        "A5": "small_municipalities",
        "A6": "medium_municipalities",
        "A7": "large_municipalities",
        "A8": "huge_municipalities",
        "A9": "cities",
        "A10": "ratio_urban_inhabitants",
        "A11": "average_salary",
        "A12": "unemployment_rate_1995",
        "A13": "unemployment_rate_1996",
        "A14": "entrepreneurs_per_1000_inhabitants",
        "A15": "crimes_committed_1995",
        "A16": "crimes_committed_1996",
    },
    inplace=True,
)

for col in [
    "unemployment_rate_1995",
    "unemployment_rate_1996",
    "crimes_committed_1995",
    "crimes_committed_1996",
]:
    districts_df[col] = pd.to_numeric(districts_df[col], errors="coerce")

districts_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 77 entries, 0 to 76
Data columns (total 16 columns):
 #   Column                              Non-Null Count  Dtype  
---  ------                              --------------  -----  
 0   district_id                         77 non-null     int64  
 1   district_name                       77 non-null     object 
 2   region                              77 non-null     object 
 3   inhabitants                         77 non-null     int64  
 4   small_municipalities                77 non-null     int64  
 5   medium_municipalities               77 non-null     int64  
 6   large_municipalities                77 non-null     int64  
 7   huge_municipalities                 77 non-null     int64  
 8   cities                              77 non-null     int64  
 9   ratio_urban_inhabitants             77 non-null     float64
 10  average_salary                      77 non-null     int64  
 11  unemployment_rate_1995              76 non-null     float64
 12  unemployment_rate_1996              77 non-null     float64
 13  entrepreneurs_per_1000_inhabitants  77 non-null     int64  
 14  crimes_committed_1995               76 non-null     float64
 15  crimes_committed_1996               77 non-null     int64  
dtypes: float64(4), int64(10), object(2)
memory usage: 9.8+ KB

It appears as if there is 1 null value for unemployment rate in 1995 and crimes committed in 1995.

districts_df["crimes_committed_1995"].corr(districts_df["crimes_committed_1996"])

0.9984261453682067

The correlation between crimes committed in 1995 and 1996 is 0.99, which is very high. This implies that the number of crimes committed in 1995 is a good predictor for the number of crimes committed in 1996. Since 1995 is missing a data point we will impute the missing value with crimes committed in 1996.

districts_df["unemployment_rate_1995"].corr(districts_df["unemployment_rate_1996"])

0.9815212338478934

The same goes for the unemployment rate in 1995 and 1996.

We can impute the values therefore using a linear regression model to approximate the missing values.

from sklearn.linear_model import LinearRegression

districts_df_imputed = districts_df.copy()

X = districts_df_imputed[['crimes_committed_1996']].values.reshape(-1, 1)
y = districts_df_imputed['crimes_committed_1995'].values.reshape(-1, 1)
mask = ~np.isnan(y).flatten()
reg_model = LinearRegression()
reg_model.fit(X[mask], y[mask])
imputed_values = reg_model.predict(X[~mask])
districts_df_imputed.loc[districts_df_imputed['crimes_committed_1995'].isnull(), 'crimes_committed_1995'] = imputed_values.flatten()

X = districts_df_imputed[['unemployment_rate_1996']].values.reshape(-1, 1)
y = districts_df_imputed['unemployment_rate_1995'].values.reshape(-1, 1)
mask = ~np.isnan(y).flatten()
reg_model = LinearRegression()
reg_model.fit(X[mask], y[mask])
imputed_values = reg_model.predict(X[~mask])
districts_df_imputed.loc[districts_df_imputed['unemployment_rate_1995'].isnull(), 'unemployment_rate_1995'] = imputed_values.flatten()

districts_df = districts_df_imputed.copy()
districts_df.dropna(inplace=True)

districts_df.isnull().sum()

district_id                           0
district_name                         0
region                                0
inhabitants                           0
small_municipalities                  0
medium_municipalities                 0
large_municipalities                  0
huge_municipalities                   0
cities                                0
ratio_urban_inhabitants               0
average_salary                        0
unemployment_rate_1995                0
unemployment_rate_1996                0
entrepreneurs_per_1000_inhabitants    0
crimes_committed_1995                 0
crimes_committed_1996                 0
dtype: int64

Now there are no missing values in the dataset and the integrity of the data is preserved.

# todo add some basic eda here
districts_df.head()

	district_id	district_name	region	inhabitants	small_municipalities	medium_municipalities	large_municipalities	huge_municipalities	cities	ratio_urban_inhabitants	average_salary	unemployment_rate_1995	unemployment_rate_1996	entrepreneurs_per_1000_inhabitants	crimes_committed_1995	crimes_committed_1996
0	1	Hl.m. Praha	Prague	1204953	0	0	0	1	1	100.0	12541	0.29	0.43	167	85677.0	99107
1	2	Benesov	central Bohemia	88884	80	26	6	2	5	46.7	8507	1.67	1.85	132	2159.0	2674
2	3	Beroun	central Bohemia	75232	55	26	4	1	5	41.7	8980	1.95	2.21	111	2824.0	2813
3	4	Kladno	central Bohemia	149893	63	29	6	2	6	67.4	9753	4.64	5.05	109	5244.0	5892
4	5	Kolin	central Bohemia	95616	65	30	4	1	6	51.4	9307	3.85	4.43	118	2616.0	3040

districts_df.describe()

	district_id	inhabitants	small_municipalities	medium_municipalities	large_municipalities	huge_municipalities	cities	ratio_urban_inhabitants	average_salary	unemployment_rate_1995	unemployment_rate_1996	entrepreneurs_per_1000_inhabitants	crimes_committed_1995	crimes_committed_1996
count	77.000000	7.700000e+01	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000
mean	39.000000	1.338849e+05	48.623377	24.324675	6.272727	1.727273	6.259740	63.035065	9031.675325	3.156135	3.787013	116.129870	4808.230490	5030.831169
std	22.371857	1.369135e+05	32.741829	12.780991	4.015222	1.008338	2.435497	16.221727	790.202347	1.685779	1.908480	16.608773	9830.616565	11270.796786
min	1.000000	4.282100e+04	0.000000	0.000000	0.000000	0.000000	1.000000	33.900000	8110.000000	0.290000	0.430000	81.000000	818.000000	888.000000
25%	20.000000	8.585200e+04	22.000000	16.000000	4.000000	1.000000	5.000000	51.900000	8512.000000	1.790000	2.310000	105.000000	1879.000000	2122.000000
50%	39.000000	1.088710e+05	49.000000	25.000000	6.000000	2.000000	6.000000	59.800000	8814.000000	2.830000	3.600000	113.000000	2879.000000	3040.000000
75%	58.000000	1.390120e+05	71.000000	32.000000	8.000000	2.000000	8.000000	73.500000	9317.000000	4.010000	4.790000	126.000000	4484.000000	4595.000000
max	77.000000	1.204953e+06	151.000000	70.000000	20.000000	5.000000	11.000000	100.000000	12541.000000	7.340000	9.400000	167.000000	85677.000000	99107.000000

districts_df.nunique()

district_id                           77
district_name                         77
region                                 8
inhabitants                           77
small_municipalities                  53
medium_municipalities                 36
large_municipalities                  17
huge_municipalities                    6
cities                                11
ratio_urban_inhabitants               70
average_salary                        76
unemployment_rate_1995                71
unemployment_rate_1996                73
entrepreneurs_per_1000_inhabitants    44
crimes_committed_1995                 76
crimes_committed_1996                 76
dtype: int64

plot_numerical_distributions(districts_df, ["average_salary", "crimes_committed_1995"])

# plot how many inhabitants each region has, group by region first then sum
plt.figure(figsize=(10, 6))
sum_inhabitants_per_region = districts_df.groupby("region")["inhabitants"].sum()
sum_inhabitants_per_region.plot(kind="bar")
plt.title("Total Number of Inhabitants per Region")
plt.xlabel("Region")
plt.ylabel("Total Number of Inhabitants")

# add exact numbers to the bar 
for i, v in enumerate(sum_inhabitants_per_region):
    plt.text(i, v + 10000, str(v), ha="center")

plt.show()

Looking at the distribution of inhabitants per region we can see that the majority of inhabitants are in the region of Prague. This is not surprising as Prague is the capital of the Czech Republic and is the most populous region.

A quick google search seems to show that the inhabitants per region are approximately correctly set. Here an example for (Prague)[https://www.google.com/search?q=prague+city+population+1999&sourceid=chrome&ie=UTF-8]

This is not the case for south Moravia and north Moravia, which appear to be overestimated, however we couldn’t find a very reliable source for this information.

1.3 Data Relationships

Following the documentation of the dataset, there are multiple relationships that need to be validated. https://sorry.vse.cz/~berka/challenge/PAST/index.html

The ERD according to the descriptions on https://sorry.vse.cz/~berka/challenge/PAST/index.html

This ERD shows how the data appears in the dataset:

In order to also validate the relationships from a algorithmic perspective, we can use the following code:

# verify 1:1 relationships between CLIENT, LOAN and DISPOSITION
assert dispositions_df[
    "client_id"
].is_unique, "Each client_id should appear exactly once in the DISPOSITION DataFrame."
assert loans_df[
    "account_id"
].is_unique, "Each account_id should appear exactly once in the LOAN DataFrame."

# verify 1:M relationships between ACCOUNT and DISPOSITION
# assert dispositions['account_id'].is_unique == False, "An account_id should appear more than once in the DISPOSITION DataFrame."
assert (
    dispositions_df["account_id"].is_unique == True
), "An account_id should appear once in the DISPOSITION DataFrame."
# TODO check if in accordance to decision to remove disponents from dispositions

# verify each district_id in ACCOUNT and CLIENT exists in DISTRICT
assert set(accounts_df["district_id"]).issubset(
    set(districts_df["district_id"])
), "All district_ids in ACCOUNT should exist in DISTRICT."
assert set(clients_df["district_id"]).issubset(
    set(districts_df["district_id"])
), "All district_ids in CLIENT should exist in DISTRICT."

# verify each account_id in DISPOSITION, ORDER, TRANSACTION, and LOAN exists in ACCOUNT
assert set(dispositions_df["account_id"]).issubset(
    set(accounts_df["account_id"])
), "All account_ids in DISPOSITION should exist in ACCOUNT."
assert set(orders_df["account_id"]).issubset(
    set(accounts_df["account_id"])
), "All account_ids in ORDER should exist in ACCOUNT."
assert set(transactions_df["account_id"]).issubset(
    set(accounts_df["account_id"])
), "All account_ids in TRANSACTION should exist in ACCOUNT."
assert set(loans_df["account_id"]).issubset(
    set(accounts_df["account_id"])
), "All account_ids in LOAN should exist in ACCOUNT."

# verify each client_id in DISPOSITION exists in CLIENT
assert set(dispositions_df["client_id"]).issubset(
    set(clients_df["client_id"])
), "All client_ids in DISPOSITION should exist in CLIENT."

# verify each disp_id in CARD exists in DISPOSITION
assert set(cards_df["disp_id"]).issubset(
    set(dispositions_df["disp_id"])
), "All disp_ids in CARD should exist in DISPOSITION."

2 Data Preparation: Non-Transactional Data

This section covers on how we prepare the non-transactional data in order to create the golden record.

orders_pivot_df = orders_df.pivot_table(
    index="account_id",
    columns="k_symbol",
    values="debited_amount",
    aggfunc="sum",
    fill_value=0,
)

orders_pivot_df.columns = [
    f"k_symbol_debited_sum_{col.lower()}" for col in orders_pivot_df.columns
]
orders_pivot_df = orders_pivot_df.reset_index()
orders_pivot_df.head()

	account_id	k_symbol_debited_sum_household	k_symbol_debited_sum_insurance_payment	k_symbol_debited_sum_leasing	k_symbol_debited_sum_loan_payment	k_symbol_debited_sum_na
0	1	2452.0	0.0	0.0	0.0	0.0
1	2	7266.0	0.0	0.0	3372.7	0.0
2	3	1135.0	3539.0	0.0	0.0	327.0
3	4	3363.0	0.0	0.0	0.0	0.0
4	5	2668.0	0.0	0.0	0.0	0.0

def merge_non_transactional_data(
    clients, districts, dispositions, accounts, orders, loans, cards
):
    # rename district_id for clarity in clients and accounts DataFrames
    clients = clients.rename(columns={"district_id": "client_district_id"})
    accounts = accounts.rename(columns={"district_id": "account_district_id"})

    # prepare districts dataframe for merge with prefix for clients and accounts
    districts_client_prefixed = districts.add_prefix("client_")
    districts_account_prefixed = districts.add_prefix("account_")

    # merge district information for clients and accounts with prefixed columns
    clients_with_districts = pd.merge(
        clients,
        districts_client_prefixed,
        left_on="client_district_id",
        right_on="client_district_id",
        how="left",
    )
    accounts_with_districts = pd.merge(
        accounts,
        districts_account_prefixed,
        left_on="account_district_id",
        right_on="account_district_id",
        how="left",
    )

    # merge cards with dispositions and prefix card-related columns to avoid confusion
    cards_prefixed = cards.add_prefix("card_")
    dispositions_with_cards = pd.merge(
        dispositions,
        cards_prefixed,
        left_on="disp_id",
        right_on="card_disp_id",
        how="left",
    )

    # merge clients (with district info) with dispositions and cards
    clients_dispositions_cards = pd.merge(
        dispositions_with_cards, clients_with_districts, on="client_id", how="left"
    )

    # merge the above with accounts (with district info) on account_id
    accounts_clients_cards = pd.merge(
        accounts_with_districts, clients_dispositions_cards, on="account_id", how="left"
    )

    # merge orders DataFrame, assuming orders might contain columns that could overlap, prefix as needed
    orders_prefixed = orders.add_prefix("order_")
    comprehensive_df_with_orders = pd.merge(
        accounts_clients_cards,
        orders_prefixed,
        left_on="account_id",
        right_on="order_account_id",
        how="left",
    )

    # merge loans with the comprehensive dataframe (now including orders) on account_id
    # prefix loan-related columns to maintain clarity
    loans_prefixed = loans.add_prefix("loan_")
    final_df = pd.merge(
        comprehensive_df_with_orders,
        loans_prefixed,
        left_on="account_id",
        right_on="loan_account_id",
        how="left",
    )

    final_df["account_created"] = pd.to_datetime(final_df["account_created"])
    final_df["card_issued"] = pd.to_datetime(final_df["card_issued"])
    final_df["has_card"] = final_df["card_issued"].notna()
    return final_df


non_transactional_df = merge_non_transactional_data(
    clients_df,
    districts_df,
    dispositions_df,
    accounts_df,
    orders_pivot_df,
    loans_df,
    cards_df,
)
non_transactional_df.to_csv("data/non_transactional.csv", index=False)
non_transactional_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4500 entries, 0 to 4499
Data columns (total 59 columns):
 #   Column                                        Non-Null Count  Dtype         
---  ------                                        --------------  -----         
 0   account_id                                    4500 non-null   int64         
 1   account_district_id                           4500 non-null   int64         
 2   account_frequency                             4500 non-null   object        
 3   account_created                               4500 non-null   datetime64[ns]
 4   account_district_name                         4500 non-null   object        
 5   account_region                                4500 non-null   object        
 6   account_inhabitants                           4500 non-null   int64         
 7   account_small_municipalities                  4500 non-null   int64         
 8   account_medium_municipalities                 4500 non-null   int64         
 9   account_large_municipalities                  4500 non-null   int64         
 10  account_huge_municipalities                   4500 non-null   int64         
 11  account_cities                                4500 non-null   int64         
 12  account_ratio_urban_inhabitants               4500 non-null   float64       
 13  account_average_salary                        4500 non-null   int64         
 14  account_unemployment_rate_1995                4500 non-null   float64       
 15  account_unemployment_rate_1996                4500 non-null   float64       
 16  account_entrepreneurs_per_1000_inhabitants    4500 non-null   int64         
 17  account_crimes_committed_1995                 4500 non-null   float64       
 18  account_crimes_committed_1996                 4500 non-null   int64         
 19  disp_id                                       4500 non-null   int64         
 20  client_id                                     4500 non-null   int64         
 21  type                                          4500 non-null   object        
 22  card_card_id                                  892 non-null    float64       
 23  card_disp_id                                  892 non-null    float64       
 24  card_type                                     892 non-null    object        
 25  card_issued                                   892 non-null    datetime64[ns]
 26  client_district_id                            4500 non-null   int64         
 27  sex                                           4500 non-null   object        
 28  birth_date                                    4500 non-null   datetime64[ns]
 29  age                                           4500 non-null   int64         
 30  client_district_name                          4500 non-null   object        
 31  client_region                                 4500 non-null   object        
 32  client_inhabitants                            4500 non-null   int64         
 33  client_small_municipalities                   4500 non-null   int64         
 34  client_medium_municipalities                  4500 non-null   int64         
 35  client_large_municipalities                   4500 non-null   int64         
 36  client_huge_municipalities                    4500 non-null   int64         
 37  client_cities                                 4500 non-null   int64         
 38  client_ratio_urban_inhabitants                4500 non-null   float64       
 39  client_average_salary                         4500 non-null   int64         
 40  client_unemployment_rate_1995                 4500 non-null   float64       
 41  client_unemployment_rate_1996                 4500 non-null   float64       
 42  client_entrepreneurs_per_1000_inhabitants     4500 non-null   int64         
 43  client_crimes_committed_1995                  4500 non-null   float64       
 44  client_crimes_committed_1996                  4500 non-null   int64         
 45  order_account_id                              3758 non-null   float64       
 46  order_k_symbol_debited_sum_household          3758 non-null   float64       
 47  order_k_symbol_debited_sum_insurance_payment  3758 non-null   float64       
 48  order_k_symbol_debited_sum_leasing            3758 non-null   float64       
 49  order_k_symbol_debited_sum_loan_payment       3758 non-null   float64       
 50  order_k_symbol_debited_sum_na                 3758 non-null   float64       
 51  loan_loan_id                                  682 non-null    float64       
 52  loan_account_id                               682 non-null    float64       
 53  loan_granted_date                             682 non-null    datetime64[ns]
 54  loan_amount                                   682 non-null    float64       
 55  loan_duration                                 682 non-null    float64       
 56  loan_monthly_payments                         682 non-null    float64       
 57  loan_status                                   682 non-null    object        
 58  has_card                                      4500 non-null   bool          
dtypes: bool(1), datetime64[ns](4), float64(21), int64(24), object(9)
memory usage: 2.0+ MB

3 Exploratory Data Analysis

3.1 Non-transactional Data

3.1.1 Card Holders

plt.figure()
plt.title("Number of Clients by Card Type")
sns.barplot(
    x=["No Card", "Classic/Gold Card Holders", "Junior Card Holders"],
    y=[
        non_transactional_df["card_type"].isna().sum(),
        non_transactional_df["card_type"].isin(["gold", "classic"]).sum(),
        non_transactional_df["card_type"].eq("junior").sum(),
    ],
)
# ensure that the number of clients is shown on the bars
for i, v in enumerate(
    [
        non_transactional_df["card_type"].isna().sum(),
        non_transactional_df["card_type"].isin(["gold", "classic"]).sum(),
        non_transactional_df["card_type"].eq("junior").sum(),
    ]
):
    plt.text(i, v + 10, str(v), ha="center", va="bottom")

plt.show()

Looking at the distribution of card holders in general we can see that the most clients are not in a possession of a credit card.

plt.figure()
plt.title(
    f'Distribution of Age for Junior Card Holders\n total count = {len(non_transactional_df[non_transactional_df["card_type"] == "junior"])}'
)
sns.histplot(
    non_transactional_df[non_transactional_df["card_type"] == "junior"]["age"],
    kde=True,
    bins=30,
)
plt.xlabel("Age of Client (presumably in 1999)")
plt.show()

Looking at the age distribution of Junior Card holders paints a picture on this group, however only looking at the current age may be misleading as we need to understand how old they were when the card was issued to determine if they could have been eligble for a Classic/Gold card (at least 18 when the card was issued).

non_transactional_df["card_issued"] = pd.to_datetime(
    non_transactional_df["card_issued"]
)

non_transactional_df["age_at_card_issuance"] = (
    non_transactional_df["card_issued"] - non_transactional_df["birth_date"]
)
non_transactional_df["age_at_card_issuance"] = (
    non_transactional_df["age_at_card_issuance"].dt.days // 365
)

plt.figure()
plt.title(
    f'Distribution of Age at Card Issuance for Junior Card Holders\n total count = {len(non_transactional_df[non_transactional_df["card_type"] == "junior"])}'
)
sns.histplot(
    non_transactional_df[non_transactional_df["card_type"] == "junior"][
        "age_at_card_issuance"
    ],
    kde=True,
    bins=30,
)
plt.xlabel("Age at Card Issuance")
plt.show()

Here we can see that roughly 1/3 of the Junior Card holders were not of legal age (assuming legal age is 18) when receiving their Junior Card.

plt.figure()
plt.title(
    f"Distribution of Age at Card Issuance for All Card Types\n total count = {len(non_transactional_df)}"
)
sns.histplot(
    non_transactional_df[non_transactional_df["card_type"] == "junior"][
        "age_at_card_issuance"
    ],
    kde=True,
    bins=10,
    color="blue",
    label="Junior Card Holders",
)
sns.histplot(
    non_transactional_df[non_transactional_df["card_type"] != "junior"][
        "age_at_card_issuance"
    ],
    kde=True,
    bins=30,
    color="red",
    label="Non-Junior Card Holders",
)
plt.legend()
plt.xlabel("Age at Card Issuance")
plt.show()

Comparing the age at issue date between Junior and non-Junior (Classic/Gold) card holders shows that there is no overlap between the two groups, which makes intutively sense.

Therefore removing the subset of Junior Cards seems as valid as there is no reason to believe that there are Junior Cards issued wrongly, the subset being relatively small compared to the remaining issued cards and the fact that our target is specifically Classic/Gold Card owners.

before_len = len(non_transactional_df)
non_transactional_df = non_transactional_df[
    non_transactional_df["card_type"] != "junior"
]
data_reduction["Junior Card Holders"] = -(before_len - len(non_transactional_df))
del before_len

3.1.2 Time factors on Card Status

The time between creating an account and issuing a card may also be important when filtering customers based on their history. We should avoid filtering out potentially interesting periods and understand how the timespans between account creation and card issuance are distributed.

non_transactional_w_cards_df = non_transactional_df[
    non_transactional_df["card_issued"].notna()
    & non_transactional_df["account_created"].notna()
]
non_transactional_w_cards_df["duration_days"] = (
    non_transactional_w_cards_df["card_issued"]
    - non_transactional_w_cards_df["account_created"]
).dt.days

plt.figure(figsize=(8, 6))
sns.histplot(
    non_transactional_w_cards_df["duration_days"], bins=50, edgecolor="black", kde=True
)
plt.title("Distribution of Duration Between Account Creation and Card Issuance")
plt.xlabel("Duration in Days")
plt.ylabel("Frequency")
plt.tight_layout()
plt.show()

/tmp/ipykernel_1878/17211290.py:5: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

The histogram displays a distribution with multiple peaks, indicating that there are several typical time frames for card issuance after account creation. The highest peak occurs within the first 250 days, suggesting that a significant number of cards are issued during this period. The frequency decreases as duration increases, with noticeable peaks that may correspond to specific processing batch cycles or policy changes over time. The distribution also has a long tail, suggesting that in some cases, card issuance can take a very long time.

Analyzing the length of time a client has been with the bank in relation to their account creation date and card ownership can provide valuable insights for a bank’s customer relationship management and product targeting strategies. Long-standing clients may exhibit different banking behaviors, such as product adoption and loyalty patterns, compared to newer clients.

max_account_creation_date = non_transactional_df["card_issued"].max()

non_transactional_df["client_tenure_years_relative"] = (
    max_account_creation_date - non_transactional_df["account_created"]
).dt.days / 365.25

plt.figure()
ax = sns.histplot(
    data=non_transactional_df,
    x="client_tenure_years_relative",
    hue="has_card",
    multiple="stack",
    binwidth=1,
    stat="percent",
)

# Call the function to add labels
add_percentage_labels(ax, non_transactional_df["has_card"].unique())

# Additional plot formatting
plt.title("Client Tenure Relative to Latest Card Issued Date and Card Ownership")
plt.xlabel("Client Tenure (Years, Relative to Latest Card Issuance)")
plt.ylabel("Percentage of Clients")

# Display the plot
plt.show()

The bar chart shows the tenure of clients in years, categorized by whether they own a credit card (True) or not (False). Each bar represents the percentage of clients within a specific tenure range, allowing for comparison of the distribution of card ownership among clients with different lengths of association with the bank.

3.1.3 Demographics

Using the available demographic data, we can investigate the potential correlation between demographic data and card status. The average salary may indicate a difference between cardholders and non-cardholders, as it is reasonable to assume that cardholders have a higher average salary than non-cardholders.

plt.figure()
sns.boxplot(x="has_card", y="client_average_salary", data=non_transactional_df)
plt.title("Average Salary in Client's Region by Card Ownership")
plt.xlabel("Has Card")
plt.ylabel("Average Salary")
plt.xticks([0, 1], ["No Card Owner", "Card Owner"])

plt.show()

The box plot compares the average salaries of clients who own a credit card with those who do not. Both groups have a substantial overlap in salary ranges, suggesting that while there might be a trend for card owners to have higher salaries, the difference is not significant. The median salary for card owners is slightly higher than that for non-card owners, as indicated by the median line within the respective boxes.

Both distributions have outliers on the higher end, indicating that some individuals have salaries significantly above the average in both groups. However, these outliers do not dominate the general trend.

It should also be noted that this plot assumes that the average salary of the region’s clients remained constant over the years, which is unlikely to be true.

The group of bar charts represents the distribution of credit card ownership across various demographics, showing the percentage of clients with and without cards within different age groups, sexes, and regions.

non_transactional_df["age_group"] = pd.cut(
    non_transactional_df["age"],
    bins=[0, 25, 40, 55, 70, 100],
    labels=["<25", "25-40", "40-55", "55-70", ">70"],
)

plt.figure(figsize=(8, 12))

# Age Group
plt.subplot(3, 1, 1)
age_group_counts = (
    non_transactional_df.groupby(["age_group", "has_card"]).size().unstack(fill_value=0)
)
age_group_percentages = (age_group_counts.T / age_group_counts.sum(axis=1)).T * 100
age_group_plot = age_group_percentages.plot(kind="bar", stacked=True, ax=plt.gca())
age_group_plot.set_title("Card Ownership by Age Group")
age_group_plot.set_ylabel("Percentage")
add_percentage_labels(age_group_plot, non_transactional_df["has_card"].unique())

# Sex
plt.subplot(3, 1, 2)
sex_counts = (
    non_transactional_df.groupby(["sex", "has_card"]).size().unstack(fill_value=0)
)
sex_percentages = (sex_counts.T / sex_counts.sum(axis=1)).T * 100
sex_plot = sex_percentages.plot(kind="bar", stacked=True, ax=plt.gca())
sex_plot.set_title("Card Ownership by Sex")
sex_plot.set_ylabel("Percentage")
add_percentage_labels(sex_plot, non_transactional_df["has_card"].unique())

# Client Region
plt.subplot(3, 1, 3)
region_counts = (
    non_transactional_df.groupby(["client_region", "has_card"])
    .size()
    .unstack(fill_value=0)
)
region_percentages = (region_counts.T / region_counts.sum(axis=1)).T * 100
region_plot = region_percentages.plot(kind="bar", stacked=True, ax=plt.gca())
region_plot.set_title("Card Ownership by Client Region")
region_plot.set_ylabel("Percentage")
region_plot.tick_params(axis="x", rotation=45)
add_percentage_labels(region_plot, non_transactional_df["has_card"].unique())

plt.tight_layout()
plt.show()

/tmp/ipykernel_1878/271218705.py:12: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

Card Ownership by Age Group: The bar chart displays the proportion of cardholders in different age groups. The percentage of cardholders is lowest in the age group of over 70, followed by the age group of 55-70, indicating that card ownership is more prevalent among younger demographics.

Card Ownership by Sex: The bar chart shows the breakdown of card ownership by sex. The data reveals that the percentage of cardholders is comparable between both sexes, and no significant difference is present.

Card Ownership by Region The bar chart at the bottom illustrates card ownership across different regions, showing a relatively consistent pattern among most regions.

3.1.4 Impact of Loans / Debt

simplified_loan_status_mapping = {
    "Contract finished, no problems": "Finished",
    "Contract finished, loan not paid": "Not Paid",
    "Contract running, OK thus-far": "Running",
    "Contract running, client in debt": "In Debt",
    "No Loan": "No Loan",
}

non_transactional_df["loan_status_simplified"] = non_transactional_df[
    "loan_status"
].map(simplified_loan_status_mapping)

# this variable wants to kill itself
loan_status_simplified_card_ownership_counts = (
    non_transactional_df.groupby(["loan_status_simplified", "has_card"])
    .size()
    .unstack(fill_value=0)
)
loan_status_simplified_card_ownership_percentages = (
    loan_status_simplified_card_ownership_counts.T
    / loan_status_simplified_card_ownership_counts.sum(axis=1)
).T * 100

loan_status_simplified_card_ownership_percentages.plot(
    kind="bar", stacked=True, figsize=(8, 6)
)
plt.title("Interaction Between Simplified Loan Status and Card Ownership")
plt.xlabel("Simplified Loan Status")
plt.ylabel("Percentage of Clients")
plt.xticks(rotation=45)
plt.legend(title="Has Card", labels=["No Card", "Has Card"])
plt.tight_layout()
plt.show()

3.2 Transactional Data

zero_amount_transactions_df = transactions_df[transactions_df["amount"] == 0]

zero_amount_transactions_info = {
    "total_zero_amount_transactions": len(zero_amount_transactions_df),
    "unique_accounts_with_zero_amount": zero_amount_transactions_df[
        "account_id"
    ].nunique(),
    "transaction_type_distribution": zero_amount_transactions_df[
        "transaction_type"
    ].value_counts(normalize=True),
    "operation_distribution": zero_amount_transactions_df["operation"].value_counts(
        normalize=True
    ),
    "k_symbol_distribution": zero_amount_transactions_df["k_symbol"].value_counts(
        normalize=True
    ),
}

zero_amount_transactions_info, len(zero_amount_transactions_info)

({'total_zero_amount_transactions': 14,
  'unique_accounts_with_zero_amount': 12,
  'transaction_type_distribution': transaction_type
  Withdrawal    0.714286
  Credit        0.285714
  Name: proportion, dtype: float64,
  'operation_distribution': operation
  Withdrawal in Cash    0.714286
  NA                    0.285714
  Name: proportion, dtype: float64,
  'k_symbol_distribution': k_symbol
  Sanction Interest    0.714286
  Interest Credited    0.285714
  Name: proportion, dtype: float64},
 5)

accounts_with_zero_amount_transactions = accounts_df[
    accounts_df["account_id"].isin(zero_amount_transactions_df["account_id"].unique())
]
accounts_with_zero_amount_transactions

	account_id	district_id	account_frequency	account_created
178	5369	54	MONTHLY_ISSUANCE	1993-02-25
289	5483	13	MONTHLY_ISSUANCE	1993-03-28
496	5129	68	MONTHLY_ISSUANCE	1993-06-08
513	1475	1	WEEKLY_ISSUANCE	1993-06-14
799	9337	30	MONTHLY_ISSUANCE	1993-09-13
896	102	11	MONTHLY_ISSUANCE	1993-10-16
986	8957	1	MONTHLY_ISSUANCE	1993-11-13
2033	5125	1	MONTHLY_ISSUANCE	1995-09-14
2300	9051	5	WEEKLY_ISSUANCE	1996-01-17
2651	3859	53	MONTHLY_ISSUANCE	1996-04-23
3212	6083	6	WEEKLY_ISSUANCE	1996-09-19
3342	1330	68	MONTHLY_ISSUANCE	1996-10-22

# Clean up unnecessary variables
del accounts_with_zero_amount_transactions
del zero_amount_transactions_df
del zero_amount_transactions_info

Validating first transactions where the amount equals the balance is essential for the integrity of our aggregated data analysis. This specific assertion underpins the reliability of our subsequent aggregation operations by ensuring each account’s financial history starts from a verifiable point.

def validate_first_transactions(transactions):
    """
    Validates that for each account in the transactions DataFrame, there is at least
    one transaction where the amount equals the balance on the account's first transaction date.

    Parameters:
    - transactions (pd.DataFrame): DataFrame containing transaction data with columns
      'account_id', 'date', 'amount', and 'balance'.

    Raises:
    - AssertionError: If not every account has a first transaction where the amount equals the balance.
    """

    first_dates = (
        transactions.groupby("account_id")["date"].min().reset_index(name="first_date")
    )

    first_trans = pd.merge(transactions, first_dates, how="left", on=["account_id"])

    first_trans_filtered = first_trans[
        (first_trans["date"] == first_trans["first_date"])
        & (first_trans["amount"] == first_trans["balance"])
    ]

    first_trans_filtered = first_trans_filtered.drop_duplicates(subset=["account_id"])

    unique_accounts = transactions["account_id"].nunique()
    assert (
        unique_accounts == first_trans_filtered["account_id"].nunique()
    ), "Not every account has a first transaction where the amount equals the balance."

    return "Validation successful: Each account has a first transaction where the amount equals the balance."


validate_first_transactions(transactions_df)

'Validation successful: Each account has a first transaction where the amount equals the balance.'

We can confirm the truth of the assertions made. It is certain that there is a transaction with an amount equal to the balance in the transaction history of every account on the first date.

## DEPENDENCY 1 TODO REMOVE FOR MERGE 
import json
# save transactions_df to temp as parquet

transactions_df.to_parquet("temp/transactions.parquet")
accounts_df.to_parquet("temp/accounts.parquet")
non_transactional_df.to_parquet("temp/non_transactional.parquet")

# save data reduction
with open("temp/data_reduction.json", "w") as f:
    json.dump(data_reduction, f)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from sklearn.metrics import pairwise_distances
from tqdm import tqdm
import seaborn as sns
import plotly.graph_objects as go
import json


transactions_df = pd.read_parquet("temp/transactions.parquet")
accounts_df = pd.read_parquet("temp/accounts.parquet")
non_transactional_df = pd.read_parquet("temp/non_transactional.parquet")
with open("temp/data_reduction.json", "r") as f:
    data_reduction = json.load(f)

4 Data Preparation: Transactional Data

As we already prepared the non-transactional data in a previous section, we now focus on the transactional data. As the end goal is to have a single record per customer, we need to aggregate the transactional data. In addition, absolute temporal data such as transaction dates needs to be transformed into relative data, such as the number of months before the event of card issuance.

4.1 Set artificial issue date for non-card holders

One crucial step in the data preparation process is to set an artificial card issue date for non-card holders. This date is necessary to align the transactional data of non-card holders with that of card holders. By setting an artificial card issue date, we can create a unified timeline for all customers, enabling a more accurate comparison of transactional behaviors across different groups.

First we will explore the distribution of the months between account creation and card issuance to understand the typical timeline for card issuance after account creation.

def add_months_since_account_to_card(df):
    """Add a column to the DataFrame with the number of months between account creation and card issuance."""
    df["months_since_account_to_card"] = df.apply(
        lambda row: (
            (
                row["card_issued"].to_period("M")
                - row["account_created"].to_period("M")
            ).n
            if pd.notnull(row["card_issued"]) and pd.notnull(row["account_created"])
            else np.nan
        ),
        axis=1,
    )
    return df

As we need enough history to make a valid comparison between card holders and non-card holders, we filter out non-card holders with less than 25 months of history. This ensures that we have a sufficient amount of data to analyze and compare the transactional behaviors of both groups. Here is the reasoning behind this threshold:

• New Customer Period (12 months): We need at least one year of transactional history to capture the typical spending patterns of customers.
• One Year of History (12 months): An additional year of data provides a more comprehensive view of transactional behaviors, allowing us to identify trends and patterns more accurately across seasons and economic cycles.
• Lag Period (1 month): The month immediately preceding card issuance is excluded to avoid any potential bias caused by transactional changes due to the impending card issuance.

def filter_clients_without_sufficient_history(
    non_transactional_df, min_history_months=25
):
    if "months_since_account_to_card" not in non_transactional_df.columns:
        non_transactional_df = add_months_since_account_to_card(non_transactional_df)

    count_before = len(non_transactional_df)
    filtered_df = non_transactional_df[
        non_transactional_df["months_since_account_to_card"].isnull()
        | (non_transactional_df["months_since_account_to_card"] >= min_history_months)
    ]
    print(
        f"Filtered out {count_before - len(filtered_df)} records with less than {min_history_months} months of history. "
        f"Percentage: {(count_before - len(filtered_df)) / count_before * 100:.2f}%."
    )
    return filtered_df


before_len = len(non_transactional_df)

non_transactional_w_sufficient_history_df = filter_clients_without_sufficient_history(
    non_transactional_df
)

data_reduction["Clients without sufficient history"] = -(
    before_len - len(non_transactional_w_sufficient_history_df)
)
del before_len

Filtered out 419 records with less than 25 months of history. Percentage: 9.62%.

In this case roughly 10% of the non-card holders were filtered out due to insufficient history. This is a reasonable amount and will not impact the analysis significantly.

Next, we will explore the distribution of the months between account creation and card issuance for card holders to understand the typical timeline for card issuance after account creation.

non_transactional_w_card_df = non_transactional_w_sufficient_history_df.dropna(
    subset=["card_issued"]
).copy()

plt.figure(figsize=(8, 6))
sns.histplot(
    non_transactional_w_card_df["months_since_account_to_card"], kde=True, bins=30
)
plt.title(
    "Distribution of Months from Account Creation to Card Issuance (for Card Holders)"
)
plt.xlabel("Months")
plt.ylabel("Count")
plt.grid(True)
plt.tight_layout()
plt.show()

The plot shows a right-skewed distribution, with the majority of card holders receiving their cards roughly 25 to 30 months after account creation. On the long-tail end, some clients receive their cards after 60 months or more.

After briefly exploring the distribution of the months between account creation and card issuance for card holders, we will now set the artificial card issuance date for non-card holders. This date will be used to align the transactional data of non-card holders with that of card holders, enabling a more accurate comparison of transactional behaviors across different groups.

The following approaches were considered to match non-card holders with card holders:

1. Looking at the distributions above extract the amount of history a buyer most likely has at the issue data of the card
2. For each non buyer, find a buyer which was active in a similar time window (Jaccard similarity on the Year-Month sets). Instead of looking at the full activity of a buyer, we only look at the pre-purchase activity as there is reason to believe that clients may change their patterns after purchasing date and therefore add unwanted bias.

The second approach is chosen as it is provides an intuitive way to match clients based on their activity which is not only explainable but also provides a way to match clients based on their behavior. It strikes a balance of not finding a perfect match but a good enough match to focus on the discriminative features of the data.

Both approaches have their advantages and disadvantages. The first approach is more straightforward and less computationally intensive, but it may not capture the nuances of client behavior. The second approach is more complex and computationally intensive but offers a more nuanced view of client activity, potentially leading to better matches.

4.2 Match by similar transaction activity

The process emphasizes matching based on the timing of activity, rather than a wide array of characteristics. By identifying when both existing cardholders and non-cardholders interacted with the bank, we can infer a level of behavioral alignment that extends beyond mere transactional data. This alignment suggests a shared response to external conditions. Intuitively we are constructing tuples of non-card holders and card holders based on the similarity of their activity patterns but one of them is not a card holder yet.

Assumption: This assumes that clients active during similar periods might be influenced by the same economic and societal conditions, providing a more nuanced foundation for establishing connections between current cardholders and potential new ones.

4.2.1 Construction of the Activity Matrix

To hold the needed information about every customer we create a so called activity matrix. The resolution of the activity matrix is a binary matrix where each row represents a client and each column represents a month. A value of 1 indicates activity in a given month, while 0 indicates inactivity. Therefore we concentrate on the periods during which clients engage with the bank in the form of transactions rather than the specifics of those transactions. Here is a step-by-step breakdown of the construction process:

1. Data Aggregation: We start with transaction data, which records each client’s interactions across various months. This data includes every transaction made by both current cardholders and non-cardholders.

2. Temporal Transformation: Each transaction is associated with a specific date. These dates are then transformed into monthly periods, consolidating daily transactions into a monthly view of activity. This step simplifies the data, focusing on the presence of activity within each month rather than the specific dates or frequencies of transactions.

3. Matrix Structure: The transformed data is arranged into a matrix format. Rows represent individual clients, identified by their account IDs. Columns correspond to monthly periods, spanning the entire range of months covered by the transaction data.

4. Activity Indication: In the matrix, a cell value is set to indicate the presence of activity for a given client in a given month. If a client made one or more transactions in a month, the corresponding cell is marked to reflect this activity. The absence of transactions for a client in a month leaves the cell unmarked. Active months are represented by a ‘1’, indicating the presence of transactions, while inactive months are denoted by a ‘0’, indicating no transactions.

def prepare_activity_matrix(transactions):
    """
    Create an activity matrix from transaction data.

    The function transforms transaction data into a binary matrix that indicates
    whether an account was active in a given month.

    Parameters:
    - transactions (pd.DataFrame): A DataFrame containing the transaction data.

    Returns:
    - pd.DataFrame: An activity matrix with accounts as rows and months as columns.
    """
    transactions["month_year"] = transactions["date"].dt.to_period("M")
    transactions["active"] = 1

    activity_matrix = transactions.pivot_table(
        index="account_id", columns="month_year", values="active", fill_value=0
    )

    activity_matrix.columns = [f"active_{str(col)}" for col in activity_matrix.columns]
    return activity_matrix


def plot_activity_matrix(activity_matrix):
    activity_matrix = activity_matrix.reindex(
        activity_matrix.sum(axis=1).sort_values(ascending=False).index # sort by activity across time
    )
    
    activity_matrix.columns = activity_matrix.columns.str.replace("active_", "")
    sparse_matrix = activity_matrix.astype(bool)
    plt.figure(figsize=(8, 8))
    sns.heatmap(sparse_matrix, cmap="binary", yticklabels=False, cbar=False)
    plt.title(f"Activity Matrix across all clients sorted by account creation date")
    plt.xlabel("Month-Year")
    plt.ylabel("Accounts")
    plt.tight_layout()

    active_patch = mpatches.Patch(color='black', label='Active')
    inactive_patch = mpatches.Patch(color='white', label='Not Active')
    plt.legend(handles=[active_patch, inactive_patch], loc='upper right')

    plt.show()


activity_matrix = prepare_activity_matrix(transactions_df)
plot_activity_matrix(activity_matrix.copy())

The heatmap provided offers a visual representation of the activity matrix for clients, depicting the levels of engagement over various periods. Clients are sorted by activity, with the most active clients at the top and the least active at the bottom.

There is a distinct diagonal pattern, indicating that newer accounts (those created more recently) perhaps have fewer periods of activity. This makes sense as these accounts have not had the opportunity to transact over the earlier periods displayed on the heatmap.

Also interesting are some gaps withing the activity of some clients. This could be due to a variety of reasons, such as seasonal spending patterns or changes in financial circumstances.

4.2.2 Eligibility Criteria

After constructing the activity matrix, we check for eligibility of non-cardholders to be matched with cardholders. This ensures alignment for later model construction. The eligibility criteria are as follows:

1. Account History: Non-cardholders must have an established history of interaction, with at least 25 months of history between account creation and card issuance (12 months (= New customer period) + 13 months (= one year of history) + 1 month (Lag period)) as described above.
2. Account Creation Date: The account creation date of a non-cardholder must precede the card issuance date of the cardholder as this is a prerequisite for the matching process to work correctly when we set the issue date for non-card holders following the intuition that nobody can have a card before the account is created.

ELIGIBILITY_THRESHOLD_HIST_MONTHS = 25


def check_eligibility_for_matching(non_cardholder, cardholder, verbose=False):
    """
    Determine if a non-cardholder is eligible for matching with a cardholder.

    This function checks whether the card issuance to a cardholder occurred at least
    25 months after the non-cardholder's account was created.

    Parameters:
    - non_cardholder (pd.Series): A data series containing the non-cardholder's details.
    - cardholder (pd.Series): A data series containing the cardholder's details.
    - verbose (bool): If True, print detailed eligibility information. Default is False.

    Returns:
    - bool: True if the non-cardholder is eligible for matching, False otherwise.
    """
    if cardholder["card_issued"] <= non_cardholder["account_created"]:
        return False

    period_diff = (
        cardholder["card_issued"].to_period("M")
        - non_cardholder["account_created"].to_period("M")
    ).n

    if verbose:
        print(
            f"Card issued: {cardholder['card_issued']}, Account created: {non_cardholder['account_created']}, Period diff: {period_diff}, Eligible: {period_diff >= ELIGIBILITY_THRESHOLD_HIST_MONTHS}"
        )

    return period_diff >= ELIGIBILITY_THRESHOLD_HIST_MONTHS

4.2.3 Matching Process

Next up we will implement the matching process. Our matching utilizes the Jaccard similarity index to compare activity patterns: We compare a vector representing an existing cardholder’s monthly activity against a matrix of non-cardholders’ activity patterns. Here we only consider the activity from the first transaction period across all customers to the card issue date.

The Jaccard similarity index is calculated as the intersection of active months divided by the union of active months between the two clients. This index ranges from 0 to 1, with higher values indicating greater overlap in activity patterns.

\[J(A, B) = \frac{|A \cap B|}{|A \cup B|}\]

The function select_non_cardholders randomly selects a non-cardholder match for a cardholder from the top N eligible candidates. The selection process is based on the Jaccard similarity scores calculated between the cardholder and non-cardholders. The function performs the following steps:

1. Sorting by Similarity: The function sorts the Jaccard distances between a cardholder and non-cardholders to identify the top N similar non-cardholders.
2. Random Selection: From the top N similar non-cardholders, the function randomly selects one non-cardholder match for the cardholder. This random selection helps avoid bias and ensures a fair matching process.

def select_non_cardholders(
    distances,
    eligible_non_cardholders,
    matches,
    matched_applicants,
    cardholder,
    without_card_activity,
    top_n,
):
    """
    Randomly select a non-cardholder match for a cardholder from the top N eligible candidates.

    Parameters:
    - distances (np.array): An array of Jaccard distances between a cardholder and non-cardholders.
    - eligible_non_cardholders (list): A list of indices for non-cardholders who are eligible for matching.
    - matches (list): A list to which the match will be appended.
    - matched_applicants (set): A set of indices for non-cardholders who have already been matched.
    - cardholder (pd.Series): The data series of the current cardholder.
    - without_card_activity (pd.DataFrame): A DataFrame of non-cardholders without card issuance.
    - top_n (int): The number of top similar non-cardholders to consider for matching.

    Returns:
    - None: The matches list is updated in place with the selected match (Object by reference).
    """
    eligible_distances = distances[eligible_non_cardholders]
    sorted_indices = np.argsort(eligible_distances)[:top_n]

    if sorted_indices.size > 0:
        selected_index = np.random.choice(sorted_indices)
        actual_selected_index = eligible_non_cardholders[selected_index]

        if actual_selected_index not in matched_applicants:
            matched_applicants.add(actual_selected_index)
            applicant = without_card_activity.iloc[actual_selected_index]
            similarity = 1 - eligible_distances[selected_index]

            matches.append(
                (cardholder["client_id"], applicant["client_id"], similarity)
            )

The function match_cardholders_with_non_cardholders brings together the data preparation, matching process, and match selection steps. It performs the following operations:

1. Data Preparation: The function prepares the activity matrix and splits the non-cardholders into two groups: those with and without cards.
2. Matching Process: For each cardholder, the function calculates the Jaccard similarity between their activity pattern and those of eligible non-cardholders. It then selects the top N similar non-cardholders and randomly assigns one match per cardholder.
3. Match Selection: The function selects a non-cardholder match for each cardholder based on the Jaccard similarity scores. It ensures that each non-cardholder is matched only once and that the top N similar non-cardholders are considered for matching.
   1. The selection among the top N similar non-cardholders is done randomly to avoid bias. This process is defined in the select_non_cardholders function.
   2. The function also checks for the eligibility as defined above.
   3. If no eligible non-cardholders are found, the function prints a warning message.
4. Output: The function returns a list of tuples containing the matched cardholder and non-cardholder client IDs along with their similarity scores.

def match_cardholders_with_non_cardholders(non_transactional, transactions, top_n=5):
    """
    Match cardholders with non-cardholders based on the similarity of their activity patterns.

    The function creates an activity matrix, identifies eligible non-cardholders, calculates
    the Jaccard similarity to find matches, and randomly selects one match per cardholder
    from the top N similar non-cardholders.

    Parameters:
    - non_transactional (pd.DataFrame): A DataFrame containing non-cardholders.
    - transactions (pd.DataFrame): A DataFrame containing transactional data.
    - top_n (int): The number of top similar non-cardholders to consider for matching.

    Returns:
    - list: A list of tuples with the cardholder and matched non-cardholder client IDs and similarity scores.
    """
    with_card = non_transactional[non_transactional["card_issued"].notna()]
    without_card = non_transactional[non_transactional["card_issued"].isna()]

    activity_matrix = prepare_activity_matrix(transactions)

    with_card_activity = with_card.join(activity_matrix, on="account_id", how="left")
    without_card_activity = without_card.join(
        activity_matrix, on="account_id", how="left"
    )

    matched_non_cardholders = set()
    matches = []

    for idx, cardholder in tqdm(
        with_card_activity.iterrows(),
        total=len(with_card_activity),
        desc="Matching cardholders",
    ):
        issue_period = cardholder["card_issued"].to_period("M")
        eligible_cols = [
            col
            for col in activity_matrix
            if col.startswith("active") and pd.Period(col.split("_")[1]) <= issue_period
        ]

        if not eligible_cols:
            print(
                f"No eligible months found for cardholder client_id {cardholder['client_id']}."
            )
            continue
        
        cardholder_vector = cardholder[eligible_cols].values.reshape(1, -1)
        non_cardholder_matrix = without_card_activity[eligible_cols].values
        
        cardholder_vector = np.where(cardholder_vector > 0, 1, 0).astype(bool)
        non_cardholder_matrix = np.where(non_cardholder_matrix > 0, 1, 0).astype(bool)

        assert (
            cardholder_vector.shape[1] == non_cardholder_matrix.shape[1]
        ), "Dimension mismatch between cardholder and applicant activity matrix."

        distances = pairwise_distances(
            cardholder_vector, non_cardholder_matrix, 
            metric="jaccard", n_jobs=-1 
        ).flatten()
        eligible_non_cardholders = [
            i
            for i, applicant in without_card_activity.iterrows()
            if check_eligibility_for_matching(applicant, cardholder)
            and i not in matched_non_cardholders
        ]

        if eligible_non_cardholders:
            select_non_cardholders(
                distances,
                eligible_non_cardholders,
                matches,
                matched_non_cardholders,
                cardholder,
                without_card_activity,
                top_n,
            )
        else:
            print(
                f"No eligible non-cardholders found for cardholder client_id {cardholder['client_id']}."
            )

    return matches

The matching process is executed, and the results are stored in the matched_non_card_holders_df DataFrame. The percentage of clients with a card issued before and after matching is calculated to assess the impact of the matching process. We expect the percentage of clients with a card issued to increase by 100% after matching, as each non-cardholder should be matched with a cardholder.

matched_non_card_holders_df = match_cardholders_with_non_cardholders(
    non_transactional_w_sufficient_history_df, transactions_df
)

percentage_before_matching = non_transactional_w_sufficient_history_df["card_issued"].notna().mean() * 100
print(f"Percentage of clients with card issued: {percentage_before_matching:.2f}%")

Matching cardholders:   0%|          | 0/328 [00:00<?, ?it/s]Matching cardholders:   0%|          | 1/328 [00:00<03:03,  1.78it/s]Matching cardholders:   1%|          | 2/328 [00:00<02:21,  2.30it/s]Matching cardholders:   1%|          | 3/328 [00:01<02:08,  2.52it/s]Matching cardholders:   1%|          | 4/328 [00:01<01:55,  2.81it/s]Matching cardholders:   2%|▏         | 5/328 [00:01<01:51,  2.89it/s]Matching cardholders:   2%|▏         | 6/328 [00:02<01:51,  2.88it/s]Matching cardholders:   2%|▏         | 7/328 [00:02<01:50,  2.90it/s]Matching cardholders:   2%|▏         | 8/328 [00:02<01:50,  2.89it/s]Matching cardholders:   3%|▎         | 9/328 [00:03<01:38,  3.24it/s]Matching cardholders:   3%|▎         | 10/328 [00:03<01:30,  3.53it/s]Matching cardholders:   3%|▎         | 11/328 [00:03<01:36,  3.30it/s]Matching cardholders:   4%|▎         | 12/328 [00:04<01:40,  3.15it/s]Matching cardholders:   4%|▍         | 13/328 [00:04<01:42,  3.06it/s]Matching cardholders:   4%|▍         | 14/328 [00:04<01:39,  3.15it/s]Matching cardholders:   5%|▍         | 15/328 [00:05<01:42,  3.06it/s]Matching cardholders:   5%|▍         | 16/328 [00:05<01:32,  3.38it/s]Matching cardholders:   5%|▌         | 17/328 [00:05<01:25,  3.66it/s]Matching cardholders:   5%|▌         | 18/328 [00:05<01:20,  3.83it/s]Matching cardholders:   6%|▌         | 19/328 [00:06<01:27,  3.52it/s]Matching cardholders:   6%|▌         | 20/328 [00:06<01:32,  3.34it/s]Matching cardholders:   6%|▋         | 21/328 [00:06<01:36,  3.19it/s]Matching cardholders:   7%|▋         | 22/328 [00:07<01:39,  3.08it/s]Matching cardholders:   7%|▋         | 23/328 [00:07<01:54,  2.67it/s]Matching cardholders:   7%|▋         | 24/328 [00:07<01:51,  2.73it/s]Matching cardholders:   8%|▊         | 25/328 [00:08<01:49,  2.76it/s]Matching cardholders:   8%|▊         | 26/328 [00:08<01:37,  3.09it/s]Matching cardholders:   8%|▊         | 27/328 [00:08<01:31,  3.30it/s]Matching cardholders:   9%|▊         | 28/328 [00:09<01:33,  3.20it/s]Matching cardholders:   9%|▉         | 29/328 [00:09<01:33,  3.21it/s]Matching cardholders:   9%|▉         | 30/328 [00:09<01:35,  3.11it/s]Matching cardholders:   9%|▉         | 31/328 [00:10<01:34,  3.15it/s]Matching cardholders:  10%|▉         | 32/328 [00:10<01:26,  3.41it/s]Matching cardholders:  10%|█         | 33/328 [00:10<01:28,  3.33it/s]Matching cardholders:  10%|█         | 34/328 [00:10<01:28,  3.32it/s]Matching cardholders:  11%|█         | 35/328 [00:11<01:31,  3.19it/s]Matching cardholders:  11%|█         | 36/328 [00:11<01:25,  3.43it/s]Matching cardholders:  11%|█▏        | 37/328 [00:11<01:30,  3.23it/s]Matching cardholders:  12%|█▏        | 38/328 [00:12<01:33,  3.10it/s]Matching cardholders:  12%|█▏        | 39/328 [00:12<01:29,  3.22it/s]Matching cardholders:  12%|█▏        | 40/328 [00:12<01:30,  3.17it/s]Matching cardholders:  12%|█▎        | 41/328 [00:13<01:32,  3.11it/s]Matching cardholders:  13%|█▎        | 42/328 [00:13<01:33,  3.07it/s]Matching cardholders:  13%|█▎        | 43/328 [00:13<01:34,  3.00it/s]Matching cardholders:  13%|█▎        | 44/328 [00:14<01:39,  2.86it/s]Matching cardholders:  14%|█▎        | 45/328 [00:14<01:31,  3.08it/s]Matching cardholders:  14%|█▍        | 46/328 [00:14<01:26,  3.26it/s]Matching cardholders:  14%|█▍        | 47/328 [00:15<01:29,  3.14it/s]Matching cardholders:  15%|█▍        | 48/328 [00:15<01:31,  3.05it/s]Matching cardholders:  15%|█▍        | 49/328 [00:15<01:33,  3.00it/s]Matching cardholders:  15%|█▌        | 50/328 [00:16<01:32,  3.02it/s]Matching cardholders:  16%|█▌        | 51/328 [00:16<01:29,  3.08it/s]Matching cardholders:  16%|█▌        | 52/328 [00:16<01:23,  3.31it/s]Matching cardholders:  16%|█▌        | 53/328 [00:17<01:25,  3.20it/s]Matching cardholders:  16%|█▋        | 54/328 [00:17<01:20,  3.40it/s]Matching cardholders:  17%|█▋        | 55/328 [00:17<01:22,  3.29it/s]Matching cardholders:  17%|█▋        | 56/328 [00:17<01:26,  3.15it/s]Matching cardholders:  17%|█▋        | 57/328 [00:18<01:28,  3.06it/s]Matching cardholders:  18%|█▊        | 58/328 [00:18<01:25,  3.14it/s]Matching cardholders:  18%|█▊        | 59/328 [00:18<01:19,  3.40it/s]Matching cardholders:  18%|█▊        | 60/328 [00:19<01:18,  3.40it/s]Matching cardholders:  19%|█▊        | 61/328 [00:19<01:15,  3.53it/s]Matching cardholders:  19%|█▉        | 62/328 [00:19<01:20,  3.31it/s]Matching cardholders:  19%|█▉        | 63/328 [00:20<01:23,  3.18it/s]Matching cardholders:  20%|█▉        | 64/328 [00:20<01:24,  3.11it/s]Matching cardholders:  20%|█▉        | 65/328 [00:20<01:26,  3.05it/s]Matching cardholders:  20%|██        | 66/328 [00:21<01:24,  3.11it/s]Matching cardholders:  20%|██        | 67/328 [00:21<01:35,  2.73it/s]Matching cardholders:  21%|██        | 68/328 [00:21<01:33,  2.78it/s]Matching cardholders:  21%|██        | 69/328 [00:22<01:28,  2.93it/s]Matching cardholders:  21%|██▏       | 70/328 [00:22<01:25,  3.01it/s]Matching cardholders:  22%|██▏       | 71/328 [00:22<01:27,  2.95it/s]Matching cardholders:  22%|██▏       | 72/328 [00:23<01:21,  3.16it/s]Matching cardholders:  22%|██▏       | 73/328 [00:23<01:23,  3.06it/s]Matching cardholders:  23%|██▎       | 74/328 [00:23<01:20,  3.14it/s]Matching cardholders:  23%|██▎       | 75/328 [00:24<01:21,  3.10it/s]Matching cardholders:  23%|██▎       | 76/328 [00:24<01:23,  3.01it/s]Matching cardholders:  23%|██▎       | 77/328 [00:24<01:23,  3.02it/s]Matching cardholders:  24%|██▍       | 78/328 [00:25<01:22,  3.04it/s]Matching cardholders:  24%|██▍       | 79/328 [00:25<01:23,  2.97it/s]Matching cardholders:  24%|██▍       | 80/328 [00:25<01:22,  2.99it/s]Matching cardholders:  25%|██▍       | 81/328 [00:26<01:18,  3.15it/s]Matching cardholders:  25%|██▌       | 82/328 [00:26<01:18,  3.13it/s]Matching cardholders:  25%|██▌       | 83/328 [00:26<01:20,  3.04it/s]Matching cardholders:  26%|██▌       | 84/328 [00:27<01:21,  2.98it/s]Matching cardholders:  26%|██▌       | 85/328 [00:27<01:19,  3.06it/s]Matching cardholders:  26%|██▌       | 86/328 [00:27<01:15,  3.23it/s]Matching cardholders:  27%|██▋       | 87/328 [00:27<01:12,  3.31it/s]Matching cardholders:  27%|██▋       | 88/328 [00:28<01:08,  3.51it/s]Matching cardholders:  27%|██▋       | 89/328 [00:28<01:06,  3.61it/s]Matching cardholders:  27%|██▋       | 90/328 [00:28<01:18,  3.05it/s]Matching cardholders:  28%|██▊       | 91/328 [00:29<01:19,  3.00it/s]Matching cardholders:  28%|██▊       | 92/328 [00:29<01:18,  3.00it/s]Matching cardholders:  28%|██▊       | 93/328 [00:29<01:19,  2.97it/s]Matching cardholders:  29%|██▊       | 94/328 [00:30<01:19,  2.94it/s]Matching cardholders:  29%|██▉       | 95/328 [00:30<01:19,  2.91it/s]Matching cardholders:  29%|██▉       | 96/328 [00:30<01:20,  2.90it/s]Matching cardholders:  30%|██▉       | 97/328 [00:31<01:18,  2.93it/s]Matching cardholders:  30%|██▉       | 98/328 [00:31<01:19,  2.90it/s]Matching cardholders:  30%|███       | 99/328 [00:32<01:18,  2.91it/s]Matching cardholders:  30%|███       | 100/328 [00:32<01:11,  3.20it/s]Matching cardholders:  31%|███       | 101/328 [00:32<01:09,  3.25it/s]Matching cardholders:  31%|███       | 102/328 [00:32<01:12,  3.12it/s]Matching cardholders:  31%|███▏      | 103/328 [00:33<01:13,  3.04it/s]Matching cardholders:  32%|███▏      | 104/328 [00:33<01:14,  3.00it/s]Matching cardholders:  32%|███▏      | 105/328 [00:33<01:11,  3.12it/s]Matching cardholders:  32%|███▏      | 106/328 [00:34<01:12,  3.06it/s]Matching cardholders:  33%|███▎      | 107/328 [00:34<01:11,  3.07it/s]Matching cardholders:  33%|███▎      | 108/328 [00:34<01:06,  3.32it/s]Matching cardholders:  33%|███▎      | 109/328 [00:35<01:02,  3.52it/s]Matching cardholders:  34%|███▎      | 110/328 [00:35<01:06,  3.28it/s]Matching cardholders:  34%|███▍      | 111/328 [00:35<01:08,  3.15it/s]Matching cardholders:  34%|███▍      | 112/328 [00:36<01:08,  3.14it/s]Matching cardholders:  34%|███▍      | 113/328 [00:36<01:14,  2.90it/s]Matching cardholders:  35%|███▍      | 114/328 [00:36<01:13,  2.90it/s]Matching cardholders:  35%|███▌      | 115/328 [00:37<01:13,  2.90it/s]Matching cardholders:  35%|███▌      | 116/328 [00:37<01:10,  2.99it/s]Matching cardholders:  36%|███▌      | 117/328 [00:37<01:10,  2.99it/s]Matching cardholders:  36%|███▌      | 118/328 [00:38<01:04,  3.25it/s]Matching cardholders:  36%|███▋      | 119/328 [00:38<01:06,  3.14it/s]Matching cardholders:  37%|███▋      | 120/328 [00:38<01:05,  3.20it/s]Matching cardholders:  37%|███▋      | 121/328 [00:39<01:06,  3.09it/s]Matching cardholders:  37%|███▋      | 122/328 [00:39<01:02,  3.27it/s]Matching cardholders:  38%|███▊      | 123/328 [00:39<01:03,  3.25it/s]Matching cardholders:  38%|███▊      | 124/328 [00:39<01:02,  3.26it/s]Matching cardholders:  38%|███▊      | 125/328 [00:40<01:03,  3.17it/s]Matching cardholders:  38%|███▊      | 126/328 [00:40<01:05,  3.07it/s]Matching cardholders:  39%|███▊      | 127/328 [00:40<01:01,  3.28it/s]Matching cardholders:  39%|███▉      | 128/328 [00:41<00:58,  3.40it/s]Matching cardholders:  39%|███▉      | 129/328 [00:41<01:01,  3.21it/s]Matching cardholders:  40%|███▉      | 130/328 [00:41<01:02,  3.18it/s]Matching cardholders:  40%|███▉      | 131/328 [00:42<01:03,  3.08it/s]Matching cardholders:  40%|████      | 132/328 [00:42<00:59,  3.31it/s]Matching cardholders:  41%|████      | 133/328 [00:42<01:01,  3.15it/s]Matching cardholders:  41%|████      | 134/328 [00:43<01:03,  3.07it/s]Matching cardholders:  41%|████      | 135/328 [00:43<01:01,  3.12it/s]Matching cardholders:  41%|████▏     | 136/328 [00:43<01:09,  2.75it/s]Matching cardholders:  42%|████▏     | 137/328 [00:44<01:08,  2.78it/s]Matching cardholders:  42%|████▏     | 138/328 [00:44<01:07,  2.81it/s]Matching cardholders:  42%|████▏     | 139/328 [00:44<01:07,  2.82it/s]Matching cardholders:  43%|████▎     | 140/328 [00:45<01:04,  2.89it/s]Matching cardholders:  43%|████▎     | 141/328 [00:45<01:00,  3.11it/s]Matching cardholders:  43%|████▎     | 142/328 [00:45<00:59,  3.15it/s]Matching cardholders:  44%|████▎     | 143/328 [00:46<01:00,  3.05it/s]Matching cardholders:  44%|████▍     | 144/328 [00:46<01:01,  2.98it/s]Matching cardholders:  44%|████▍     | 145/328 [00:46<00:58,  3.11it/s]Matching cardholders:  45%|████▍     | 146/328 [00:47<00:58,  3.10it/s]Matching cardholders:  45%|████▍     | 147/328 [00:47<00:59,  3.03it/s]Matching cardholders:  45%|████▌     | 148/328 [00:47<00:57,  3.15it/s]Matching cardholders:  45%|████▌     | 149/328 [00:48<00:55,  3.21it/s]Matching cardholders:  46%|████▌     | 150/328 [00:48<00:57,  3.09it/s]Matching cardholders:  46%|████▌     | 151/328 [00:48<00:54,  3.23it/s]Matching cardholders:  46%|████▋     | 152/328 [00:49<00:56,  3.12it/s]Matching cardholders:  47%|████▋     | 153/328 [00:49<00:53,  3.27it/s]Matching cardholders:  47%|████▋     | 154/328 [00:49<00:54,  3.20it/s]Matching cardholders:  47%|████▋     | 155/328 [00:49<00:56,  3.09it/s]Matching cardholders:  48%|████▊     | 156/328 [00:50<00:56,  3.02it/s]Matching cardholders:  48%|████▊     | 157/328 [00:50<00:57,  2.98it/s]Matching cardholders:  48%|████▊     | 158/328 [00:51<00:56,  3.01it/s]Matching cardholders:  48%|████▊     | 159/328 [00:51<01:00,  2.80it/s]Matching cardholders:  49%|████▉     | 160/328 [00:51<00:57,  2.93it/s]Matching cardholders:  49%|████▉     | 161/328 [00:52<00:54,  3.09it/s]Matching cardholders:  49%|████▉     | 162/328 [00:52<00:53,  3.13it/s]Matching cardholders:  50%|████▉     | 163/328 [00:52<00:50,  3.26it/s]Matching cardholders:  50%|█████     | 164/328 [00:52<00:53,  3.09it/s]Matching cardholders:  50%|█████     | 165/328 [00:53<00:53,  3.04it/s]Matching cardholders:  51%|█████     | 166/328 [00:53<00:54,  2.98it/s]Matching cardholders:  51%|█████     | 167/328 [00:53<00:54,  2.97it/s]Matching cardholders:  51%|█████     | 168/328 [00:54<00:54,  2.96it/s]Matching cardholders:  52%|█████▏    | 169/328 [00:54<00:54,  2.92it/s]Matching cardholders:  52%|█████▏    | 170/328 [00:55<00:54,  2.92it/s]Matching cardholders:  52%|█████▏    | 171/328 [00:55<00:53,  2.92it/s]Matching cardholders:  52%|█████▏    | 172/328 [00:55<00:53,  2.93it/s]Matching cardholders:  53%|█████▎    | 173/328 [00:55<00:50,  3.09it/s]Matching cardholders:  53%|█████▎    | 174/328 [00:56<00:48,  3.16it/s]Matching cardholders:  53%|█████▎    | 175/328 [00:56<00:49,  3.07it/s]Matching cardholders:  54%|█████▎    | 176/328 [00:56<00:50,  3.00it/s]Matching cardholders:  54%|█████▍    | 177/328 [00:57<00:50,  2.96it/s]Matching cardholders:  54%|█████▍    | 178/328 [00:57<00:51,  2.93it/s]Matching cardholders:  55%|█████▍    | 179/328 [00:58<00:50,  2.93it/s]Matching cardholders:  55%|█████▍    | 180/328 [00:58<00:50,  2.91it/s]Matching cardholders:  55%|█████▌    | 181/328 [00:58<00:48,  3.02it/s]Matching cardholders:  55%|█████▌    | 182/328 [00:59<00:52,  2.79it/s]Matching cardholders:  56%|█████▌    | 183/328 [00:59<00:51,  2.81it/s]Matching cardholders:  56%|█████▌    | 184/328 [00:59<00:51,  2.82it/s]Matching cardholders:  56%|█████▋    | 185/328 [01:00<00:49,  2.87it/s]Matching cardholders:  57%|█████▋    | 186/328 [01:00<00:48,  2.93it/s]Matching cardholders:  57%|█████▋    | 187/328 [01:00<00:48,  2.90it/s]Matching cardholders:  57%|█████▋    | 188/328 [01:01<00:48,  2.89it/s]Matching cardholders:  58%|█████▊    | 189/328 [01:01<00:46,  3.00it/s]Matching cardholders:  58%|█████▊    | 190/328 [01:01<00:48,  2.86it/s]Matching cardholders:  58%|█████▊    | 191/328 [01:02<00:47,  2.86it/s]Matching cardholders:  59%|█████▊    | 192/328 [01:02<00:46,  2.95it/s]Matching cardholders:  59%|█████▉    | 193/328 [01:02<00:45,  2.99it/s]Matching cardholders:  59%|█████▉    | 194/328 [01:03<00:43,  3.05it/s]Matching cardholders:  59%|█████▉    | 195/328 [01:03<00:44,  2.99it/s]Matching cardholders:  60%|█████▉    | 196/328 [01:03<00:43,  3.01it/s]Matching cardholders:  60%|██████    | 197/328 [01:04<00:42,  3.05it/s]Matching cardholders:  60%|██████    | 198/328 [01:04<00:43,  2.99it/s]Matching cardholders:  61%|██████    | 199/328 [01:04<00:42,  3.03it/s]Matching cardholders:  61%|██████    | 200/328 [01:05<00:42,  2.98it/s]Matching cardholders:  61%|██████▏   | 201/328 [01:05<00:43,  2.95it/s]Matching cardholders:  62%|██████▏   | 202/328 [01:05<00:42,  2.99it/s]Matching cardholders:  62%|██████▏   | 203/328 [01:06<00:44,  2.84it/s]Matching cardholders:  62%|██████▏   | 204/328 [01:06<00:43,  2.84it/s]Matching cardholders:  62%|██████▎   | 205/328 [01:07<00:47,  2.57it/s]Matching cardholders:  63%|██████▎   | 206/328 [01:07<00:46,  2.63it/s]Matching cardholders:  63%|██████▎   | 207/328 [01:07<00:45,  2.69it/s]Matching cardholders:  63%|██████▎   | 208/328 [01:08<00:42,  2.82it/s]Matching cardholders:  64%|██████▎   | 209/328 [01:08<00:42,  2.83it/s]Matching cardholders:  64%|██████▍   | 210/328 [01:08<00:41,  2.83it/s]Matching cardholders:  64%|██████▍   | 211/328 [01:09<00:44,  2.65it/s]Matching cardholders:  65%|██████▍   | 212/328 [01:09<00:42,  2.74it/s]Matching cardholders:  65%|██████▍   | 213/328 [01:09<00:41,  2.76it/s]Matching cardholders:  65%|██████▌   | 214/328 [01:10<00:40,  2.79it/s]Matching cardholders:  66%|██████▌   | 215/328 [01:10<00:40,  2.81it/s]Matching cardholders:  66%|██████▌   | 216/328 [01:10<00:39,  2.83it/s]Matching cardholders:  66%|██████▌   | 217/328 [01:11<00:39,  2.84it/s]Matching cardholders:  66%|██████▋   | 218/328 [01:11<00:38,  2.88it/s]Matching cardholders:  67%|██████▋   | 219/328 [01:11<00:37,  2.93it/s]Matching cardholders:  67%|██████▋   | 220/328 [01:12<00:37,  2.91it/s]Matching cardholders:  67%|██████▋   | 221/328 [01:12<00:36,  2.90it/s]Matching cardholders:  68%|██████▊   | 222/328 [01:13<00:36,  2.93it/s]Matching cardholders:  68%|██████▊   | 223/328 [01:13<00:35,  2.97it/s]Matching cardholders:  68%|██████▊   | 224/328 [01:13<00:35,  2.95it/s]Matching cardholders:  69%|██████▊   | 225/328 [01:14<00:35,  2.93it/s]Matching cardholders:  69%|██████▉   | 226/328 [01:14<00:35,  2.91it/s]Matching cardholders:  69%|██████▉   | 227/328 [01:14<00:34,  2.89it/s]Matching cardholders:  70%|██████▉   | 228/328 [01:15<00:38,  2.62it/s]Matching cardholders:  70%|██████▉   | 229/328 [01:15<00:36,  2.73it/s]Matching cardholders:  70%|███████   | 230/328 [01:15<00:35,  2.76it/s]Matching cardholders:  70%|███████   | 231/328 [01:16<00:34,  2.79it/s]Matching cardholders:  71%|███████   | 232/328 [01:16<00:34,  2.81it/s]Matching cardholders:  71%|███████   | 233/328 [01:16<00:33,  2.85it/s]Matching cardholders:  71%|███████▏  | 234/328 [01:17<00:32,  2.85it/s]Matching cardholders:  72%|███████▏  | 235/328 [01:17<00:32,  2.86it/s]Matching cardholders:  72%|███████▏  | 236/328 [01:17<00:32,  2.85it/s]Matching cardholders:  72%|███████▏  | 237/328 [01:18<00:31,  2.86it/s]Matching cardholders:  73%|███████▎  | 238/328 [01:18<00:31,  2.86it/s]Matching cardholders:  73%|███████▎  | 239/328 [01:19<00:31,  2.86it/s]Matching cardholders:  73%|███████▎  | 240/328 [01:19<00:30,  2.86it/s]Matching cardholders:  73%|███████▎  | 241/328 [01:19<00:30,  2.87it/s]Matching cardholders:  74%|███████▍  | 242/328 [01:20<00:30,  2.85it/s]Matching cardholders:  74%|███████▍  | 243/328 [01:20<00:29,  2.86it/s]Matching cardholders:  74%|███████▍  | 244/328 [01:20<00:29,  2.87it/s]Matching cardholders:  75%|███████▍  | 245/328 [01:21<00:28,  2.87it/s]Matching cardholders:  75%|███████▌  | 246/328 [01:21<00:28,  2.86it/s]Matching cardholders:  75%|███████▌  | 247/328 [01:21<00:28,  2.86it/s]Matching cardholders:  76%|███████▌  | 248/328 [01:22<00:28,  2.86it/s]Matching cardholders:  76%|███████▌  | 249/328 [01:22<00:27,  2.88it/s]Matching cardholders:  76%|███████▌  | 250/328 [01:22<00:27,  2.87it/s]Matching cardholders:  77%|███████▋  | 251/328 [01:23<00:29,  2.61it/s]Matching cardholders:  77%|███████▋  | 252/328 [01:23<00:28,  2.70it/s]Matching cardholders:  77%|███████▋  | 253/328 [01:23<00:27,  2.75it/s]Matching cardholders:  77%|███████▋  | 254/328 [01:24<00:26,  2.79it/s]Matching cardholders:  78%|███████▊  | 255/328 [01:24<00:26,  2.80it/s]Matching cardholders:  78%|███████▊  | 256/328 [01:25<00:25,  2.82it/s]Matching cardholders:  78%|███████▊  | 257/328 [01:25<00:24,  2.84it/s]Matching cardholders:  79%|███████▊  | 258/328 [01:25<00:24,  2.85it/s]Matching cardholders:  79%|███████▉  | 259/328 [01:26<00:24,  2.85it/s]Matching cardholders:  79%|███████▉  | 260/328 [01:26<00:23,  2.85it/s]Matching cardholders:  80%|███████▉  | 261/328 [01:26<00:23,  2.85it/s]Matching cardholders:  80%|███████▉  | 262/328 [01:27<00:23,  2.84it/s]Matching cardholders:  80%|████████  | 263/328 [01:27<00:22,  2.84it/s]Matching cardholders:  80%|████████  | 264/328 [01:27<00:22,  2.84it/s]Matching cardholders:  81%|████████  | 265/328 [01:28<00:22,  2.84it/s]Matching cardholders:  81%|████████  | 266/328 [01:28<00:21,  2.85it/s]Matching cardholders:  81%|████████▏ | 267/328 [01:28<00:21,  2.85it/s]Matching cardholders:  82%|████████▏ | 268/328 [01:29<00:21,  2.85it/s]Matching cardholders:  82%|████████▏ | 269/328 [01:29<00:20,  2.86it/s]Matching cardholders:  82%|████████▏ | 270/328 [01:29<00:20,  2.86it/s]Matching cardholders:  83%|████████▎ | 271/328 [01:30<00:20,  2.85it/s]Matching cardholders:  83%|████████▎ | 272/328 [01:30<00:19,  2.85it/s]Matching cardholders:  83%|████████▎ | 273/328 [01:31<00:19,  2.85it/s]Matching cardholders:  84%|████████▎ | 274/328 [01:31<00:20,  2.59it/s]Matching cardholders:  84%|████████▍ | 275/328 [01:31<00:19,  2.67it/s]Matching cardholders:  84%|████████▍ | 276/328 [01:32<00:19,  2.72it/s]Matching cardholders:  84%|████████▍ | 277/328 [01:32<00:18,  2.77it/s]Matching cardholders:  85%|████████▍ | 278/328 [01:32<00:17,  2.79it/s]Matching cardholders:  85%|████████▌ | 279/328 [01:33<00:17,  2.80it/s]Matching cardholders:  85%|████████▌ | 280/328 [01:33<00:16,  2.83it/s]Matching cardholders:  86%|████████▌ | 281/328 [01:33<00:16,  2.83it/s]Matching cardholders:  86%|████████▌ | 282/328 [01:34<00:16,  2.83it/s]Matching cardholders:  86%|████████▋ | 283/328 [01:34<00:15,  2.84it/s]Matching cardholders:  87%|████████▋ | 284/328 [01:34<00:15,  2.84it/s]Matching cardholders:  87%|████████▋ | 285/328 [01:35<00:15,  2.85it/s]Matching cardholders:  87%|████████▋ | 286/328 [01:35<00:14,  2.86it/s]Matching cardholders:  88%|████████▊ | 287/328 [01:36<00:14,  2.86it/s]Matching cardholders:  88%|████████▊ | 288/328 [01:36<00:14,  2.85it/s]Matching cardholders:  88%|████████▊ | 289/328 [01:36<00:13,  2.85it/s]Matching cardholders:  88%|████████▊ | 290/328 [01:37<00:13,  2.86it/s]Matching cardholders:  89%|████████▊ | 291/328 [01:37<00:12,  2.86it/s]Matching cardholders:  89%|████████▉ | 292/328 [01:37<00:12,  2.86it/s]Matching cardholders:  89%|████████▉ | 293/328 [01:38<00:12,  2.86it/s]Matching cardholders:  90%|████████▉ | 294/328 [01:38<00:11,  2.86it/s]Matching cardholders:  90%|████████▉ | 295/328 [01:38<00:11,  2.86it/s]Matching cardholders:  90%|█████████ | 296/328 [01:39<00:11,  2.84it/s]Matching cardholders:  91%|█████████ | 297/328 [01:39<00:12,  2.57it/s]Matching cardholders:  91%|█████████ | 298/328 [01:40<00:11,  2.65it/s]Matching cardholders:  91%|█████████ | 299/328 [01:40<00:10,  2.71it/s]Matching cardholders:  91%|█████████▏| 300/328 [01:40<00:10,  2.76it/s]Matching cardholders:  92%|█████████▏| 301/328 [01:41<00:09,  2.79it/s]Matching cardholders:  92%|█████████▏| 302/328 [01:41<00:09,  2.80it/s]Matching cardholders:  92%|█████████▏| 303/328 [01:41<00:08,  2.82it/s]Matching cardholders:  93%|█████████▎| 304/328 [01:42<00:08,  2.84it/s]Matching cardholders:  93%|█████████▎| 305/328 [01:42<00:08,  2.83it/s]Matching cardholders:  93%|█████████▎| 306/328 [01:42<00:07,  2.84it/s]Matching cardholders:  94%|█████████▎| 307/328 [01:43<00:07,  2.85it/s]Matching cardholders:  94%|█████████▍| 308/328 [01:43<00:07,  2.84it/s]Matching cardholders:  94%|█████████▍| 309/328 [01:43<00:06,  2.85it/s]Matching cardholders:  95%|█████████▍| 310/328 [01:44<00:06,  2.84it/s]Matching cardholders:  95%|█████████▍| 311/328 [01:44<00:05,  2.85it/s]Matching cardholders:  95%|█████████▌| 312/328 [01:44<00:05,  2.85it/s]Matching cardholders:  95%|█████████▌| 313/328 [01:45<00:05,  2.84it/s]Matching cardholders:  96%|█████████▌| 314/328 [01:45<00:04,  2.85it/s]Matching cardholders:  96%|█████████▌| 315/328 [01:45<00:04,  2.86it/s]Matching cardholders:  96%|█████████▋| 316/328 [01:46<00:04,  2.84it/s]Matching cardholders:  97%|█████████▋| 317/328 [01:46<00:03,  2.85it/s]Matching cardholders:  97%|█████████▋| 318/328 [01:47<00:03,  2.85it/s]Matching cardholders:  97%|█████████▋| 319/328 [01:47<00:03,  2.81it/s]Matching cardholders:  98%|█████████▊| 320/328 [01:47<00:03,  2.55it/s]Matching cardholders:  98%|█████████▊| 321/328 [01:48<00:02,  2.64it/s]Matching cardholders:  98%|█████████▊| 322/328 [01:48<00:02,  2.69it/s]Matching cardholders:  98%|█████████▊| 323/328 [01:48<00:01,  2.75it/s]Matching cardholders:  99%|█████████▉| 324/328 [01:49<00:01,  2.77it/s]Matching cardholders:  99%|█████████▉| 325/328 [01:49<00:01,  2.81it/s]Matching cardholders:  99%|█████████▉| 326/328 [01:49<00:00,  2.81it/s]Matching cardholders: 100%|█████████▉| 327/328 [01:50<00:00,  2.82it/s]Matching cardholders: 100%|██████████| 328/328 [01:50<00:00,  2.83it/s]Matching cardholders: 100%|██████████| 328/328 [01:50<00:00,  2.96it/s]

Percentage of clients with card issued: 8.33%

Last but not least we set the artificial card issue date for each non-cardholder based on the matching results.

def set_artificial_issue_dates(non_transactional_df, matches):
    """
    Augment the non-transactional DataFrame with artificial card issue dates based on matching results.

    Each matched non-cardholder is assigned a card issue date corresponding to their matched
    cardholder. The 'has_card' flag for each non-cardholder is updated accordingly.

    Parameters:
    - non_transactional_df (pd.DataFrame): The DataFrame of non-cardholders to augment.
    - matches (list): A list of tuples containing the matched cardholder and non-cardholder IDs and similarity scores.

    Returns:
    - pd.DataFrame: The augmented DataFrame with artificial card issue dates.
    """
    augmented_df = non_transactional_df.copy()
    augmented_df["has_card"] = True

    for cardholder_id, non_cardholder_id, _ in matches:
        card_issue_date = augmented_df.loc[
            augmented_df["client_id"] == cardholder_id, "card_issued"
        ].values[0]
        augmented_df.loc[
            augmented_df["client_id"] == non_cardholder_id, ["card_issued", "has_card"]
        ] = [card_issue_date, False]

    return augmented_df

matched_non_card_holders_w_issue_date_df = set_artificial_issue_dates(
    non_transactional_w_sufficient_history_df, matched_non_card_holders_df
)

percentage_after_matching = matched_non_card_holders_w_issue_date_df["card_issued"].notna().mean() * 100
assert np.isclose(percentage_after_matching, percentage_before_matching * 2), "Percentage of clients with card issued after matching should be double the initial percentage."
print(f"Percentage of clients with card issued after matching: {percentage_after_matching:.2f}%")
print(f"Percentage without card issued after matching: {(1 - percentage_after_matching / 100) * 100:.2f}%")

Percentage of clients with card issued after matching: 16.67%
Percentage without card issued after matching: 83.33%

After each non-cardholder got the artifical card issued date assigned we drop the remaining non-cardholders without a match.

before_len = len(matched_non_card_holders_w_issue_date_df)

matched_non_card_holders_w_issue_date_df = (
    matched_non_card_holders_w_issue_date_df.dropna(subset=["card_issued"])
)

data_reduction["Non-cardholders without match"] = -(
    before_len - len(matched_non_card_holders_w_issue_date_df)
)

print(f"Filtered out {before_len - len(matched_non_card_holders_w_issue_date_df)} non-cardholders without a match.")
del before_len

Filtered out 3280 non-cardholders without a match.

In total 83% of the non-card holders were filtered out due to ineligibility or not being matched with a card holder.

4.3 Monthly Summary of Transactions

After matching cardholders with non-cardholders and setting artificial card issue dates, we aggregate the transactional data on a monthly basis to remove the transactional nature of the data. The monthly aggregation makes sense for multiple reasons:

• Monthly aggregation standardizes the time frame across which we analyze transactions, allowing us to compare transactional behaviors consistently across all accounts.
• Aggregating data on a monthly level illuminates patterns that daily data might obscure. It enables us to discern trends over a broader time scale, capturing cyclical behaviors, seasonal effects, and response to macroeconomic events.
• Daily transaction data can be “noisy” with random fluctuations. By considering monthly totals and averages, we reduce this noise, revealing underlying trends more clearly.

The function aggregate_transactions_monthly simplifies financial transactions by summarizing them every month for each account. Here’s a simplified explanation of how it works:

1. Sorting Transactions: First, the function arranges the transactions chronologically by account_id and date within the transactions_df DataFrame. This helps in organizing transactions for each account by date.

2. Monthly Grouping: It converts each transaction’s date into a monthly period. This categorizes transactions by the month and year they occurred, making it easier to group them monthly.

3. Aggregation of Monthly Data: The function groups these transactions by account_id and the month column. For each group, it calculates:

   • volume: Total transaction amount for the month.
   • total_abs_amount: Total of absolute values of all transactions, showing the total money movement regardless of direction.
   • transaction_count: Number of transactions to show how active the account was.
   • Counts of positive and negative transactions, indicating money coming in and going out.
   • Other statistics like average, median, minimum, maximum, and standard deviation of transaction amounts, which provide insights into how transaction amounts vary.
   • Counts of different transaction types, operations, and symbols, showing the variety of transactions.

4. Cumulative Balance Calculation: Finally, the function calculates a running total of the volume to track how the account balance changes over time.

This method is effective for understanding the financial behavior of accounts on a monthly basis, helping in further analyses and model building.

def aggregate_transactions_monthly(df):
    """
    Aggregate financial transaction data on a monthly basis per account.

    Parameters:
    - df (pd.DataFrame): DataFrame containing financial transaction data with 'account_id', 'date', and other relevant columns.

    - validate (bool): If True, validate the aggregated data. Default is True.

    Returns:
    - pd.DataFrame: Monthly aggregated financial transaction data per account.
    """
    df_sorted = df.sort_values(by=["account_id", "date"]) # sort by account_id and date to ensure correct running balance calculation
    df_sorted["month"] = df_sorted["date"].dt.to_period("M")

    monthly_aggregated_data = (
        df_sorted.groupby(["account_id", "month"])
        .agg(
            volume=("amount", "sum"),
            total_abs_amount=("amount", lambda x: x.abs().sum()),
            transaction_count=("amount", "count"),
            positive_transaction_count=("amount", lambda x: (x >= 0).sum()),
            negative_transaction_count=("amount", lambda x: (x < 0).sum()),
            average_amount=("amount", "mean"),
            median_amount=("amount", "median"),
            min_amount=("amount", "min"),
            max_amount=("amount", "max"),
            std_amount=("amount", "std"),
            type_count=("transaction_type", "nunique"),
            operation_count=("operation", "nunique"),
            k_symbol_count=("k_symbol", "nunique"),
        )
        .reset_index()
        .sort_values(by=["account_id", "month"])
    )
    
    monthly_aggregated_data["balance"] = monthly_aggregated_data.groupby("account_id")[
        "volume"
    ].cumsum()
    
    return monthly_aggregated_data

agg_transactions_monthly_df = aggregate_transactions_monthly(transactions_df)
agg_transactions_monthly_df.describe()

	account_id	volume	total_abs_amount	transaction_count	positive_transaction_count	negative_transaction_count	average_amount	median_amount	min_amount	max_amount	std_amount	type_count	operation_count	k_symbol_count	balance
count	185057.000000	185057.000000	185057.000000	185057.000000	185057.000000	185057.000000	185057.000000	185057.000000	185057.000000	185057.000000	176803.000000	185057.000000	185057.000000	185057.000000	185057.000000
mean	2799.983292	1065.354397	33815.492309	5.708079	2.189017	3.519062	451.659265	-372.421445	-9607.378249	14756.009580	9030.305445	1.921181	3.568965	3.719649	34474.787632
std	2331.861909	12509.136299	37724.985550	2.417842	0.726115	2.173427	2479.100575	1933.445907	10746.883348	12958.692736	7402.806514	0.269457	0.832363	1.085701	19799.443508
min	1.000000	-101550.300000	14.600000	1.000000	0.000000	0.000000	-37000.000000	-37000.000000	-87400.000000	-37000.000000	0.000000	1.000000	1.000000	1.000000	-41125.800000
25%	1172.000000	-2266.600000	9659.500000	4.000000	2.000000	2.000000	-379.566667	-785.000000	-13428.000000	4756.000000	3283.937059	2.000000	3.000000	3.000000	20405.600000
50%	2375.000000	1058.100000	22933.100000	5.000000	2.000000	3.000000	220.260000	-14.600000	-6177.000000	10929.000000	6824.369949	2.000000	4.000000	4.000000	30000.000000
75%	3576.000000	4132.200000	43668.000000	7.000000	2.000000	5.000000	878.680000	44.700000	-2672.000000	21553.000000	12622.945077	2.000000	4.000000	4.000000	44540.500000
max	11382.000000	115038.200000	609736.200000	23.000000	9.000000	16.000000	44708.000000	44708.000000	44708.000000	74812.000000	57782.701468	2.000000	6.000000	7.000000	138317.800000

The validate_monthly_aggregated_transactions function is invoked to ensure the integrity and correctness of the aggregated data through several assertions:

1. The balance should consistently increase or decrease based on whether the total monthly transaction volume is positive or negative, respectively.
2. For each account, the balance in the first month should equal the total transaction volume of that month.
3. The sum of positive and negative transaction counts must equal the total transaction count for each month.
4. The number of unique accounts in the aggregated data should match that in the original dataset.
5. The final balances of accounts in the aggregated data should closely match their last recorded transactions in the original dataset.

def validate_monthly_aggregated_transactions(aggregated_data, original_df):
    """
    Validate the integrity and correctness of aggregated monthly financial transactions.

    Parameters:
    - aggregated_data (pd.DataFrame): Aggregated monthly transaction data.
    - original_df (pd.DataFrame): Original dataset of financial transactions.

    Raises:
    - AssertionError: If validation conditions are not met.
    """
    # Assertion 1: Balance should consistently increase or decrease based on total monthly transaction volume
    assert (aggregated_data["volume"] >= 0).all() == (
        aggregated_data["balance"].diff() >= 0
    ).all(), "If the total amount is positive, the balance should go up."
    assert (aggregated_data["volume"] < 0).all() == (
        aggregated_data["balance"].diff() < 0
    ).all(), "If the total amount is negative, the balance should go down."

    # Assertion 2: Balance in the first month should equal the total transaction volume of that month
    first_month = aggregated_data.groupby("account_id").nth(0)
    assert (
        first_month["volume"] == first_month["balance"]
    ).all(), "The balance should equal the volume for the first month."

    # Assertion 3: The sum of positive and negative transaction counts should equal the total transaction count
    assert (
        aggregated_data["positive_transaction_count"]
        + aggregated_data["negative_transaction_count"]
        == aggregated_data["transaction_count"]
    ).all(), "The sum of positive and negative transaction counts should equal the total transaction count."

    # Assertion 4: The number of unique accounts in the aggregated data should match that in the original dataset
    assert (
        aggregated_data["account_id"].nunique() == original_df["account_id"].nunique()
    ), "The number of unique account_ids in the aggregated DataFrame should be the same as the original DataFrame."

    # Assertion 5: The final balances of accounts in the aggregated data should closely match their last recorded transactions in the original dataset
    assert (
        pd.merge(
            aggregated_data.groupby("account_id")
            .last()
            .reset_index()[["account_id", "balance"]],
            original_df[
                original_df.groupby("account_id")["date"].transform("max")
                == original_df["date"]
            ][["account_id", "balance"]],
            on="account_id",
            suffixes=("_final", "_last"),
        )
        .apply(
            lambda x: np.isclose(x["balance_final"], x["balance_last"], atol=5), axis=1 # allow for small differences due to floating point precision
        )
        .any()
    ), "Some accounts' final balances do not match their last transactions."


validate_monthly_aggregated_transactions(agg_transactions_monthly_df, transactions_df)

5 Exploratory Data Analysis: Aggregated Monthly Transactions

Further we explore the aggregated monthly transactions to gain insights into the financial behavior of accounts over time. We will visualize the monthly transaction volume, balance, and number of transactions to understand how these metrics evolve over time.

5.1 Monthly Balance Difference and Volume

The plot_monthly_balance_diff_and_volume function visualizes the monthly balance difference and volume for a specific account. The balance difference is calculated as the difference between the current month’s balance and the previous month’s balance. This metric helps identify the impact of monthly transactions on the account balance. The volume represents the total transaction amount for each month.

def plot_monthly_balance_diff_and_volume(
    transactions_monthly, account_id 
):
    account_transactions = transactions_monthly[
        transactions_monthly["account_id"] == account_id
    ].sort_values(by="month")
    account_transactions["balance_diff"] = account_transactions["balance"].diff()

    plt.figure(figsize=(9.5, 6))

    plt.plot(
        account_transactions["month"].astype(str),
        account_transactions["balance_diff"],
        marker="o",
        label="Balance Difference",
    )
    plt.plot(
        account_transactions["month"].astype(str),
        account_transactions["volume"],
        marker="x",
        linestyle="--",
        label="Volume",
    )

    plt.title(f"Monthly Balance Difference and Volume for Account {account_id}")
    plt.xlabel("Month")
    plt.ylabel("Value")
    plt.xticks(rotation=90, fontsize=7)
    plt.yticks(fontsize=8)
    plt.legend()
    plt.grid(True)
    plt.show()


plot_monthly_balance_diff_and_volume(agg_transactions_monthly_df, 2)

This plot gives a clear picture of how money moves in and out of an account each month and how these movements affect the overall balance. It does this by showing two things:

• Balance Difference: This line shows whether the account balance went up or down each month. If the line goes up, it means the account gained money that month. If it goes down, the account lost money.
• Volume: This line shows the total amount of money that moved in the account each month, regardless of whether it was coming in or going out.

There is a direct link between the amount of money moved (volume) and changes in the account balance. High incoming money should lead to an uptick in the balance, and lots of outgoing money should lead to a downturn. It further confirms the aggregation made in the previous step was correct.

5.2 Monthly Balance and Volume

Instead of the difference in balance, we can also look at the monthly balance and volume directly. The plot_monthly_transactions_balance_and_volume function visualizes the monthly transactions and balance for a specific account.

def plot_monthly_transactions_balance_and_volume(agg_transactions_monthly, account_id):
    account_transactions = agg_transactions_monthly[
        agg_transactions_monthly["account_id"] == account_id
    ]

    plt.figure(figsize=(9.5, 6))

    plt.plot(
        account_transactions["month"].astype(str),
        account_transactions["volume"],
        marker="o",
        label="Volume",
    )
    plt.plot(
        account_transactions["month"].astype(str),
        account_transactions["balance"],
        marker="x",
        linestyle="--",
        label="Balance",
    )

    plt.title(f"Monthly Transactions and Balance for Account {account_id}")
    plt.xlabel("Month")
    plt.ylabel("Value")
    plt.xticks(rotation=90, fontsize=7)
    plt.yticks(fontsize=8)
    plt.legend()
    plt.grid(True)
    plt.show()


plot_monthly_transactions_balance_and_volume(agg_transactions_monthly_df, 2)

This visualization offers a snapshot of an account’s activity over time by comparing money movement each month with the overall account balance. Similarly to the previous plot, it shows two key metrics:

• Volume: How much money came in or went out of the account each month. Incoming money is shown as up, and outgoing money as down.
• Balance: The total money in the account at the end of each month, showing how it’s changed over time due to the monthly transactions.

It shows how the monthly money movement impacts the account’s growing or shrinking balance. For example, a few months of high income should visibly increase the balance. It further validates the aggregation made in the previous step.

5.3 Deliverable: Closer Look at Account 14

Let’s take a closer look at the monthly transactions, balance, and volume for account 14 as requested by the task.

plot_monthly_transactions_balance_and_volume(agg_transactions_monthly_df, 14)

Account 14 shows a rather conservative transaction history. The spending habits are all withing range of 10k to -10k per month. We can see little volatility, the account shows a slight trend of growing.

5.4 Deliverable: Closer Look at Account 18

Let’s also examine the monthly transactions, balance, and volume for account 18.

plot_monthly_transactions_balance_and_volume(agg_transactions_monthly_df, 18)

Account 18 paints a different picture in comparison to account 14.

The volatility here is a lot higher, indicating a potential for a business account or high income household. Especially March 1994 to December 1994 show some volatile transaction habits.

Looking at the balance and volume per month for the accounts 14 and 18 we can notice different patterns. Account 14 shows a rather conservative transaction history with little volatility and a slight trend of growing. Account 18, on the other hand, shows a lot more volatility, indicating a potential business account or high-income household. This highlights the importance of understanding the financial behavior of accounts to identify patterns and trends that can inform decision-making. Ultimately, this is the job of the models we will build in the next steps.

6 Pivot Transactions: Rolling Up to Monthly Aggregates

Now we pivot the aggregated transaction data to have each account as a row and the months leading up to card issuance as columns. This transformation aligns with the goal for a single record per account, summarizing transactional behavior in the months before card issuance.

The pivot_transactions function aggregates monthly transaction data and merges it with non-transactional account data. It focuses on the time frame leading up to the card issuance, filtering transactions based on a specified range of months before card issuance and aggregating various transaction metrics.

We are mainly interested in the time frame 2 months to 13 months before card issuance. This range allows us to capture transactional behavior in the year leading up to the card issuance, ignoring the month directly before the card issuance to avoid any potential bias from the card issuance itself.

def pivot_transactions(
    non_transactional, transactions_monthly, months_before_card_range=(2, 13)
):
    """
    Aggregate monthly transaction data and merge it with non-transactional account data,
    focusing on the time frame leading up to the card issuance.

    This function merges monthly transaction data with non-transactional data to associate each
    transaction with the respective account and card issued date. It then filters transactions based
    on a specified range of months before card issuance and aggregates various transaction metrics.

    Parameters:
    - non_transactional (pd.DataFrame): A DataFrame containing non-transactional account data. This is only used to map card issuance dates to transactions.
    - transactions_monthly (pd.DataFrame): A DataFrame containing monthly transaction data.
    - months_before_card_range (tuple): A tuple specifying the inclusive range of months before card
                                        issuance to filter the transactions for aggregation.

    The aggregation includes the sum of volume and transaction counts, as well as the mean and other
    statistical measures of transaction amounts, for each account within the specified months before
    card issuance.

    The resulting DataFrame is pivoted to have 'account_id' as rows and the months before card
    issuance as columns, with aggregated metrics as values. Column names are constructed to
    describe the month and the metric represented.

    Returns:
    - pd.DataFrame: The final aggregated and pivoted dataset ready for analysis, with each row
                    representing an account and each column a specific metric in the months before
                    card issuance.
    """
    merged_df = transactions_monthly.merge(
        non_transactional[["account_id"]], on="account_id"
    )

    merged_df["card_issued_date"] = merged_df["account_id"].map(
        non_transactional.set_index("account_id")["card_issued"]
    )
    merged_df["months_before_card"] = merged_df.apply(
        lambda row: (row["card_issued_date"].to_period("M") - row["month"]).n, axis=1
    )

    start_month, end_month = months_before_card_range
    filtered_df = merged_df.query(f"{start_month} <= months_before_card <= {end_month}")

    aggregated_data = (
        filtered_df.groupby(["account_id", "months_before_card"])
        .agg(
            {
                "volume": "sum",
                "total_abs_amount": "sum",
                "transaction_count": "sum",
                "positive_transaction_count": "sum",
                "negative_transaction_count": "sum",
                "average_amount": "mean",
                "median_amount": "median",
                "min_amount": "min",
                "max_amount": "max",
                "std_amount": "std",
                "type_count": "sum",
                "operation_count": "sum",
                "k_symbol_count": "sum",
                "balance": "mean",
            }
        )
        .reset_index()
    )

    pivoted_data = aggregated_data.pivot(
        index="account_id", columns="months_before_card"
    )
    
    pivoted_data.columns = [
        "_".join(["M", str(col[1]), col[0]]) for col in pivoted_data.columns.values
    ]
    return pivoted_data.reset_index()

transactions_pivoted_df = pivot_transactions(
    matched_non_card_holders_w_issue_date_df, agg_transactions_monthly_df
)
transactions_pivoted_df.describe()

	account_id	M_2_volume	M_3_volume	M_4_volume	M_5_volume	M_6_volume	M_7_volume	M_8_volume	M_9_volume	M_10_volume	...	M_4_balance	M_5_balance	M_6_balance	M_7_balance	M_8_balance	M_9_balance	M_10_balance	M_11_balance	M_12_balance	M_13_balance
count	656.000000	656.000000	656.000000	656.000000	656.000000	656.000000	656.000000	656.000000	656.000000	656.000000	...	656.000000	656.000000	656.000000	656.000000	656.000000	656.000000	656.000000	655.000000	656.000000	656.000000
mean	2824.231707	-188.081402	2131.023476	-83.378354	1166.012652	-662.276982	1418.670732	1039.067683	1139.186890	-633.459299	...	43713.226677	43796.605030	42630.592378	43292.869360	41874.198628	40835.130945	39695.944055	40352.773588	39736.136585	39596.693293
std	2370.634460	12069.931426	13667.115448	13070.792019	14518.623820	13857.280237	14971.381727	13142.470175	12976.711696	15761.581790	...	20210.049464	21178.364095	20094.254246	20971.757649	20486.289111	19711.803414	19561.774856	20190.800543	19130.114948	19766.034091
min	11.000000	-76779.500000	-54322.500000	-69155.200000	-62718.000000	-67190.700000	-62113.900000	-84970.900000	-75013.500000	-66945.600000	...	1762.200000	-4575.900000	677.800000	-8789.700000	-1299.300000	-7.900000	-3269.700000	820.700000	-9843.200000	192.000000
25%	1146.750000	-2860.275000	-2118.800000	-2363.675000	-3044.425000	-3524.050000	-2881.525000	-1730.000000	-2057.200000	-3582.750000	...	26655.925000	26337.725000	26830.275000	26528.400000	25485.675000	25222.525000	24919.950000	24950.750000	25026.475000	24524.575000
50%	2330.500000	601.850000	1345.850000	1163.600000	1196.400000	841.700000	1198.750000	1328.200000	1105.650000	890.900000	...	42680.500000	42269.700000	41417.900000	41245.050000	39681.050000	38903.600000	36803.850000	37070.200000	37223.300000	36958.000000
75%	3666.000000	3583.525000	4677.725000	4250.250000	4512.375000	4456.775000	4391.850000	4530.200000	4713.650000	3944.450000	...	54942.825000	55514.200000	54229.400000	56922.150000	53359.175000	53476.650000	51744.600000	50787.050000	50391.900000	49328.125000
max	11382.000000	57541.800000	76136.000000	60272.100000	69456.400000	65912.700000	88209.200000	55601.500000	72059.700000	98041.500000	...	105453.800000	112335.700000	106459.000000	111264.300000	106472.800000	108358.900000	132286.300000	108202.400000	112159.300000	111050.900000

8 rows × 169 columns

7 Merge everything together

Finally, we merge the non-transactional data with the pivoted transactional data to create the final golden record. This record contains all relevant information for each account, including the aggregated transactional data for each month leading up to the card issuance date.

The resulting DataFrame has one row per account, with columns representing various metrics for each month before card issuance along with non-transactional data like client and account IDs, card issuance dates, and other relevant information.

We can merge the non-transactional data with the pivoted transactional data using the account_id as the common key. As each transaction is linked to an account, this key ensures that the transactional data is correctly associated with the respective account.

golden_record_df = matched_non_card_holders_w_issue_date_df.merge(
    transactions_pivoted_df, on="account_id", how="left" # left join as we 
)

data_reduction["Final Golden Record"] = len(golden_record_df)
golden_record_df.head()

	account_id	account_district_id	account_frequency	account_created	account_district_name	account_region	account_inhabitants	account_small_municipalities	account_medium_municipalities	account_large_municipalities	...	M_4_balance	M_5_balance	M_6_balance	M_7_balance	M_8_balance	M_9_balance	M_10_balance	M_11_balance	M_12_balance	M_13_balance
0	576	55	MONTHLY_ISSUANCE	1993-01-01	Brno - venkov	south Moravia	157042	49	70	18	...	35433.9	32763.3	30103.6	27455.2	39623.2	41346.8	40646.7	37953.8	35272.1	34357.8
1	3818	74	MONTHLY_ISSUANCE	1993-01-01	Ostrava - mesto	north Moravia	323870	0	0	0	...	32448.7	20928.3	48812.7	45898.7	38802.0	41537.8	30863.9	49023.8	44879.5	41579.8
2	704	55	MONTHLY_ISSUANCE	1993-01-01	Brno - venkov	south Moravia	157042	49	70	18	...	49756.6	35452.9	44789.8	33259.4	19026.1	49039.4	31829.9	35224.0	46092.2	31803.3
3	1695	76	MONTHLY_ISSUANCE	1993-01-03	Sumperk	north Moravia	127369	31	32	13	...	98674.9	97326.2	105257.0	68223.8	101761.7	62686.1	55853.1	81933.7	79168.4	99457.9
4	2379	44	MONTHLY_ISSUANCE	1993-01-10	Chrudim	east Bohemia	105606	77	26	7	...	20854.4	20774.5	19017.2	17267.0	15524.3	19539.7	15254.2	14955.0	13221.3	30056.2

5 rows × 232 columns

Looking at the first few rows of the final golden record, we can see the aggregated transactional data for each account, with columns representing various metrics for each month leading up to the card issuance date.

Additionally we can verify the uniqueness of client_id and account_id in the final DataFrame.

assert golden_record_df[
    "client_id"
].is_unique, "Each client_id should appear exactly once in the final DataFrame."

assert golden_record_df[
    "account_id"
].is_unique, "Each account_id should appear exactly once in the final DataFrame."

plt.figure()
plt.title("Number of Clients by Card Issuance Status")
sns.countplot(x="has_card", data=golden_record_df)
plt.xlabel("Card Issued")
plt.ylabel("Count")
plt.show()

Looking at the card issuance status we can see that the number of clients with a card issued is equal to the number of clients without a card issued.

plt.figure()
plt.title("Distribution of Card Issuance Dates")
sns.histplot(
    golden_record_df, x="card_issued", hue="has_card", kde=True, bins=30, alpha=0.5
)
plt.xlabel("Card Issuance Date")
plt.ylabel("Count")
plt.show()

The distribution of card issuance dates shows that the card issuance process was spread out over time, with an expected identical distribution for clients with and without cards issued. This makes sense as we set the artificial card issue date for each non-cardholder based on the matching results.

8 Data Reduction Summary

The following waterfall chart visualizes the data reduction process, highlighting the number of records retained or lost at each stage.

data_reduction_df = pd.DataFrame(
    list(data_reduction.items()), columns=["Category", "Amount"]
)
colors = ["skyblue" if amt >= 0 else "orange" for amt in data_reduction_df["Amount"]]

fig = go.Figure(
    go.Waterfall(
        name="20",
        orientation="v",
        measure=["relative"] * (len(data_reduction_df) - 1) + ["total"],
        x=data_reduction_df["Category"],
        textposition="outside",
        text=[f"{amt:,.0f}" for amt in data_reduction_df["Amount"]],
        y=data_reduction_df["Amount"],
        connector={"line": {"color": "black", "width": 2}},
        decreasing={"marker": {"color": "orange"}},
        increasing={"marker": {"color": "skyblue"}},
        totals={"marker": {"color": "skyblue"}},
    )
)

fig.update_layout(
    title="Enhanced Data Reduction Waterfall Chart",
    xaxis=dict(title="Category"),
    yaxis=dict(title="Amount", range=[0, 5500]),
    waterfallgap=0.3,
)
fig.show()

The waterfall chart provides a visual representation of the data reduction process, illustrating the number of records retained or lost at each stage. The chart shows the reduction in the number of records from the initial dataset to the final golden record, highlighting the impact of each step in the data preparation pipeline:

• Initial Dataset: The starting point with the full dataset of 4500 accounts/records.
• Junior Accounts: The removal of junior accounts, reducing the dataset by 145 records.
• Clients without sufficient history: The elimination of clients without sufficient transactional history, resulting in a reduction of 419 records.
• Non-cardholders without match: The filtering out of non-cardholders without a match, leading to a decrease of 3’280 records.
• Final Golden Record: The final dataset with 656 records, each representing a unique account with aggregated transactional data for each month leading up to the card issuance date.

9 Exploratory Data Analysis: Golden Record

With the final golden record in hand, we can now perform exploratory data analysis to gain insights into the financial behavior of cardholders and non-cardholders.

9.1 Comparing Cardholders and Non-Cardholders

We will focus on comparing the financial behavior of cardholders and non-cardholders to identify any significant differences in their transactional patterns. This analysis can help us understand how cardholders differ from non-cardholders in terms of transaction volume, balance, and other financial metrics giving us an impression of the financial behavior of cardholders and non-cardholders.

9.1.1 Trends in Financial Metrics

The function plot_trends_with_medians generates line graphs for average monthly values and annotates medians for specified ranges. This visualization helps identify trends in financial metrics over time and highlights the median values for specific periods, providing insights into the distribution of values.

golden_cardholders = golden_record_df[golden_record_df["has_card"]]
golden_non_cardholders = golden_record_df[~golden_record_df["has_card"]]


def plot_trends_with_medians(
    cardholders, non_cardholders, columns, title, median_ranges
):
    """
    Plots line graphs for average monthly values and annotates medians for specified ranges,
    adjusting x-axis indices to match the month sequence from the start.

    Parameters:
    - cardholders (pd.DataFrame): DataFrame containing data for cardholders.
    - non_cardholders (pd.DataFrame): DataFrame containing data for non-cardholders.
    - columns (list of str): List of column names ordered by time.
    - title (str): Title for the plot.
    - median_ranges (list of tuples): Each tuple contains start and end indices for calculating medians.
    """
    cardholder_avgs = cardholders[columns].mean()
    non_cardholder_avgs = non_cardholders[columns].mean()

    months = list(range(1, 1 + len(columns)))
    plt.figure()
    plt.plot(
        months,
        cardholder_avgs.values,
        marker="o",
        linestyle="-",
        color="blue",
        label="Cardholders",
    )
    plt.plot(
        months,
        non_cardholder_avgs.values,
        marker="o",
        linestyle="-",
        color="orange",
        label="Non-Cardholders",
    )

    for start, end in median_ranges:
        median_cardholder = cardholders[columns[start : end + 1]].median().median()
        median_non_cardholder = (
            non_cardholders[columns[start : end + 1]].median().median()
        )
        plt.hlines(
            median_cardholder,
            months[start],
            months[end],
            colors="darkblue",
            linestyles="--",
            label=f"Median {start+1}-{end+1} (Cardholders): {median_cardholder:.2f}",
        )
        plt.hlines(
            median_non_cardholder,
            months[start],
            months[end],
            colors="red",
            linestyles="--",
            label=f"Median {start+1}-{end+1} (Non-Cardholders): {median_non_cardholder:.2f}",
        )

    plt.title(title)
    plt.xlabel("Month")
    plt.ylabel("Value")
    plt.legend()
    plt.grid(True)
    plt.xticks(months, labels=[f"M_{month}" for month in months])  # Proper month labels
    plt.show()

9.1.2 Monthly Balance Trends

plot_trends_with_medians(
    golden_cardholders,
    golden_non_cardholders,
    [f"M_{i}_balance" for i in range(2, 14)],
    "Monthly Balance Trends",
    [(0, 2), (9, 11)]
)

Starting with the monthly balance trends, we can observe how the average balance changes over time for cardholders and non-cardholders. The line graph shows the average monthly balance for each group, with annotations indicating the median balance for specific periods.

It is interesting to note that the median balance for cardholders is consistently higher than that of non-cardholders, indicating a potential difference in financial stability or spending habits between the two groups over time.

9.1.3 Monthly Volume Trends

plot_trends_with_medians(
    golden_cardholders,
    golden_non_cardholders,
    [f"M_{i}_volume" for i in range(2, 14)],
    "Monthly Volume Trends",
    [(0, 2), (9, 11)]
)

The monthly volume trends show the average monthly transaction volume for cardholders and non-cardholders over time. The line graph illustrates how the transaction volume changes each month, with annotations highlighting the median volume for specific periods.

The median volume for cardholders is higher than that of non-cardholders, indicating a potential difference in transactional activity or spending patterns between the two groups. However, when sole looking at the volume, the difference is not as pronounced as with the balance and shows very volatile behavior. Generally this could come from the fact that the volume is the sum of all transactions, which can be naturally very volatile.

9.1.4 Monthly Transaction Count Trends

plot_trends_with_medians(
    golden_cardholders,
    golden_non_cardholders,
    [f"M_{i}_transaction_count" for i in range(2, 14)],
    "Monthly Transaction Count Trends",
    [(0, 2), (9, 11)]
)

The monthly transaction count trends show the average number of transactions per month for cardholders and non-cardholders over time. The line graph displays how the transaction count changes each month, with annotations indicating the median count for specific periods.

The median transaction count for cardholders is higher than that of non-cardholders, suggesting a difference in transactional activity or spending habits between the two groups. This is in line with the volume trends, indicating that cardholders tend to have more transactions on average than non-cardholders.

9.1.5 Monthly Positive and Negative Transaction Count Trends

plot_trends_with_medians(
    golden_cardholders,
    golden_non_cardholders,
    [f"M_{i}_positive_transaction_count" for i in range(2, 14)],
    "Monthly Positive Transaction Count Trends",
    [(0, 2), (9, 11)]
)

The monthly transaction count trends show the average number of transactions per month for cardholders and non-cardholders over time. The line graph displays how the transaction count changes each month, with annotations indicating the median count for specific periods.

The median transaction count for cardholders is only slightly higher than that of non-cardholders, indicating that the difference in transactional activity is not as pronounced for positive transactions. This suggests that both groups have similar patterns of money coming in each month.

plot_trends_with_medians(
    golden_cardholders,
    golden_non_cardholders,
    [f"M_{i}_negative_transaction_count" for i in range(2, 14)],
    "Monthly Negative Transaction Count Trends",
    [(0, 2), (9, 11)]
)

The monthly negative transaction count trends show the average number of transactions per month for cardholders and non-cardholders over time. The line graph displays how the transaction count changes each month, with annotations indicating the median count for specific periods.

The picture is pretty similar to the positive transaction count trends, indicating that both groups have similar patterns of money going out each month. This suggests that the difference in transactional activity between cardholders and non-cardholders is not as pronounced for negative transactions as well as for positive transactions.

9.1.6 Loan Amount

avg_loan_amount_cardholders = golden_cardholders["loan_amount"].mean()
avg_loan_amount_non_cardholders = golden_non_cardholders["loan_amount"].mean()

plt.figure()
plt.title("Average Loan Amount by Card Issuance Status")
sns.barplot(
    x=["Cardholders", "Non-Cardholders"],
    y=[avg_loan_amount_cardholders, avg_loan_amount_non_cardholders],
)

plt.ylabel("Average Loan Amount")
plt.show()

The average loan amount for cardholders is higher than that of non-cardholders, indicating that non-cardholders tend to have higher loan amounts. Yet, this difference is not as pronounced as with the balance, volume, and transaction count trends and not as significant.

golden_record_df.to_parquet("temp/golden_record.parquet")

## DEPENDENCY #TODO REMOVE FOR MERGE

import random

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

golden_record_df = pd.read_parquet('temp/golden_record.parquet')

np.random.seed(1337)
random.seed(1337)

10 Data Partitioning

The data is split in a 80/20 ratio for training and testing purposes. The stratification ensures that the distribution of the target variable is maintained in both sets. When actually training the models, we will additionally use cross-validation to ensure robust evaluation.

from sklearn.model_selection import train_test_split


class DataModule:
    def __init__(self, X_train, X_test, y_train, y_test, feature_columns=None):
        self.feature_columns = (
            feature_columns if feature_columns is not None else X_train.columns
        )

        self.X_train = X_train
        self.X_test = X_test
        self.y_train = y_train
        self.y_test = y_test


def create_data_module(df, feature_cols, target_col="has_card", test_size=0.2):
    X = df.drop(columns=[target_col])[feature_cols]
    y = df[target_col]

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=test_size, stratify=y, shuffle=True
    )

    return DataModule(X_train, X_test, y_train, y_test)


data_module = create_data_module(
    golden_record_df, golden_record_df.drop(columns=["has_card"]).columns
)

print(f"Train set size: {len(data_module.X_train)}")
print(f"Test set size: {len(data_module.X_test)}")

print(f"Train set distribution:\n{data_module.y_train.value_counts(normalize=True)}")
print(f"Test set distribution:\n{data_module.y_test.value_counts(normalize=True)}")

Train set size: 524
Test set size: 132
Train set distribution:
has_card
False    0.5
True     0.5
Name: proportion, dtype: float64
Test set distribution:
has_card
True     0.5
False    0.5
Name: proportion, dtype: float64

As we can see the distribution of the target variable is maintained in both sets after the split.

11 Model Construction

11.1 Pipeline for Training and Evaluation

The train_evaluate_model function is designed to streamline the process of training and evaluating machine learning models. It performs the following steps:

1. Preprocessing: The function automatically handles numerical and categorical features, imputing missing values, scaling numerical features, and one-hot encoding categorical features.
2. Model Training: The specified model is trained on the training data.
3. Cross-Validation: The model is evaluated using cross-validation with specified evaluation metrics.
4. Model Evaluation: The model is evaluated on the test set using various metrics, including accuracy, F1 score, AUC-ROC, precision, and recall.

The pipeline is flexible and can accommodate various models and feature sets, making it a versatile tool for model development and evaluation. It returns a summary of evaluation metrics for both training and test sets, as well as the true labels and predicted probabilities for the test set.

from sklearn.feature_selection import RFECV
import numpy as np
from sklearn.compose import ColumnTransformer
# FIXME validate if it would make more sense to use KNNImputer or iterative imputer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.model_selection import cross_validate, GridSearchCV
from sklearn.metrics import (
    make_scorer,
    f1_score,
    roc_auc_score,
    precision_score,
    recall_score,
)
import scikitplot as skplt
import dalex as dx


class Trainer:
    def __init__(
        self,
        data_module,
        model,
        cv=10,
        select_features=False,
        param_grid=None,
        verbose=False,
        n_jobs=-1,
    ):
        self.data_module = data_module
        self.model = model
        self.cv = cv
        self.verbose = verbose
        self.preprocessor = self._create_preprocessor()
        self.select_features = select_features
        self.param_grid = param_grid
        self.n_jobs = n_jobs
        self.pipeline = None
        self.train_metrics_report = None
        self.test_metrics_report = None

    def _create_preprocessor(self):
        numerical_features = [
            col
            for col in self.data_module.X_train.columns
            if self.data_module.X_train[col].dtype in ["int64", "float64"]
        ]
        categorical_features = [
            col
            for col in self.data_module.X_train.columns
            if col not in numerical_features
        ]

        other_features = [
            col
            for col in self.data_module.X_train.columns
            if col not in numerical_features + categorical_features
        ]
        if len(other_features) > 0:
            raise ValueError(
                f"Columns with unsupported data types found: {other_features}"
            )

        numerical_pipeline = Pipeline(
            [("imputer", SimpleImputer(strategy="mean")), ("scaler", StandardScaler())]
        )

        categorical_pipeline = Pipeline(
            [
                ("imputer", SimpleImputer(strategy="most_frequent")),
                ("onehot", OneHotEncoder(handle_unknown="ignore")),
            ]
        )

        return ColumnTransformer(
            transformers=[
                ("num", numerical_pipeline, numerical_features),
                ("cat", categorical_pipeline, categorical_features),
            ]
        )

    def fit(self):
        model_pipeline_steps = [("model", self.model)]
        if self.select_features:
            model_pipeline_steps.insert(
                0,
                (
                    "feature_selection",
                    RFECV(self.model, verbose=3 if self.verbose else 0, cv=self.cv),
                )
            )
            
        model_pipeline = Pipeline(model_pipeline_steps)
        
        if self.param_grid is not None:
            model_pipeline = GridSearchCV(
                model_pipeline,
                self.param_grid,
                cv=self.cv,
                verbose=3 if self.verbose else 0,
                n_jobs=self.n_jobs,
            )

        self.pipeline = Pipeline(
            [("preprocessor", self.preprocessor), ("model_pipeline", model_pipeline)]
        )

        self.pipeline.fit(self.data_module.X_train, self.data_module.y_train)
        return self

    @staticmethod
    def get_scoring_metrics():
        return ["accuracy", "f1_macro", "roc_auc", "precision", "recall"]

    def eval_train(self):
        scoring = {
            "accuracy": "accuracy",
            "f1_macro": make_scorer(f1_score),
            "roc_auc": "roc_auc",
            "precision": make_scorer(precision_score),
            "recall": make_scorer(recall_score),
        }

        cv_results = cross_validate(
            self.pipeline,
            self.data_module.X_train,
            self.data_module.y_train,
            scoring=scoring,
            cv=self.cv,
            return_train_score=False,
            n_jobs=self.n_jobs,
            verbose=3 if self.verbose else 0,
            return_estimator=True,
            return_indices=True,
            error_score="raise",
        )

        self.train_metrics_report = {
            metric: {
                "folds": cv_results[f"test_{metric}"].tolist(),
                "mean": cv_results[f"test_{metric}"].mean(),
                "std": cv_results[f"test_{metric}"].std(),
            }
            for metric in scoring
        }

        roc_data = []
        for i in range(self.cv):
            estimator = cv_results["estimator"][i]
            train_indices, test_indices = (
                cv_results["indices"]["train"][i],
                cv_results["indices"]["test"][i],
            )

            true_labels = self.data_module.y_train.iloc[test_indices]
            y_pred_proba = estimator.predict_proba(
                self.data_module.X_train.iloc[test_indices]
            )
            roc_data.append((true_labels, y_pred_proba))

        self.train_metrics_report["roc_data"] = roc_data

        return self

    def eval_test(self):
        X_test, y_test = self.data_module.X_test, self.data_module.y_test
        y_pred_proba = (
            self.pipeline.predict_proba(X_test)[:, 1]
            if hasattr(self.pipeline, "predict_proba")
            else np.nan
        )
        test_metrics = {
            "accuracy": self.pipeline.score(X_test, y_test),
            "f1_macro": f1_score(
                y_test, self.pipeline.predict(X_test), average="macro"
            ),
            "roc_auc": (
                roc_auc_score(y_test, y_pred_proba)
                if hasattr(self.pipeline, "predict_proba")
                else np.nan
            ),
            "precision": precision_score(y_test, self.pipeline.predict(X_test)),
            "recall": recall_score(y_test, self.pipeline.predict(X_test)),
        }
        self.test_metrics_report = {
            metric: test_metrics[metric] for metric in test_metrics
        }

        return self

    def get_pipeline(self):
        return self.pipeline

    def get_preprocessor(self):
        return self.preprocessor

    def get_train_metrics_report(self):
        return self.train_metrics_report

    def get_test_metrics_report(self):
        return self.test_metrics_report

    def get_best_params(self):
        if self.param_grid is None:
            raise ValueError(
                "No hyperparameter grid was provided during model training."
            )

        best_param = self.pipeline["model_pipeline"].best_params_
        return {key.split('__')[1]: value for key, value in best_param.items()}

    def get_selected_features(self):
        if not self.select_features:
            raise ValueError("Feature selection was not enabled during model training.")

        if (
            self.pipeline is None
            or "feature_selection"
            not in self.pipeline.named_steps["model_pipeline"].named_steps
        ):
            raise ValueError(
                "Feature selection has not been performed or the model is not fitted."
            )

        rfe = self.pipeline.named_steps["model_pipeline"].named_steps[
            "feature_selection"
        ]
        feature_mask = rfe.support_

        feature_names = self._get_feature_names_from_preprocessor()

        selected_features = [
            feature
            for feature, is_selected in zip(feature_names, feature_mask)
            if is_selected
        ]
        return [
            feature
            for feature in self.data_module.feature_columns
            if any([feature in col for col in selected_features])
        ]

    def _get_feature_names_from_preprocessor(self):
        transformers = self.preprocessor.transformers_
        feature_names = []

        for name, transformer, column in transformers:
            if hasattr(transformer, "get_feature_names_out"):
                feature_names.extend(transformer.get_feature_names_out(column))
            else:
                feature_names.extend(column)

        return feature_names

The following class handles the visualization of the model evaluation results. It provides various plots and metrics to assess the model’s performance and interpretability. The class can be used to compare multiple models and visualize their evaluation metrics side by side or individually. There is a distinction made between training and test metrics to ensure a comprehensive evaluation of the model’s performance.

from sklearn.metrics import roc_curve, classification_report, precision_recall_curve
from plotly.subplots import make_subplots

import plotly.express as px

class Visualizer:
    def __init__(self, trainer, model_name):
        self.trainer = trainer
        self.model_name = model_name

        X_train, X_test, y_train, y_test = (
            self.trainer.data_module.X_train,
            self.trainer.data_module.X_test,
            self.trainer.data_module.y_train,
            self.trainer.data_module.y_test,
        )

        self.explainer = dx.Explainer(trainer.get_pipeline(), X_test, y_test)

        self.X_test = X_test
        self.y_true = y_test
        self.y_test_pred_proba = trainer.get_pipeline().predict_proba(X_test)

    @staticmethod
    def compare_evaluation_metrics(visualizers):
        model_names = [viz.model_name for viz in visualizers]

        metrics = Trainer.get_scoring_metrics()
        means = {metric: [] for metric in metrics}
        stds = {metric: [] for metric in metrics}
        for viz in visualizers:
            train_metrics = viz.trainer.get_train_metrics_report()
            for metric in metrics:
                means[metric].append(np.mean(train_metrics[metric]["folds"]))
                stds[metric].append(np.std(train_metrics[metric]["folds"]))

        n_groups = len(metrics)
        bar_width = 0.15
        index = np.arange(n_groups)
        opacity = 0.8

        plt.figure(figsize=(9, 6))
        colors = plt.cm.viridis(np.linspace(0, 1, len(model_names)))

        for i, model_name in enumerate(model_names):
            bar_positions = index + bar_width * i
            bar_values = [means[metric][i] for metric in metrics]
            error_values = [stds[metric][i] for metric in metrics]

            bars = plt.bar(
                bar_positions,
                bar_values,
                bar_width,
                alpha=opacity,
                color=colors[i],
                yerr=error_values,
                capsize=5,
                label=model_name,
            )

            for bar, error in zip(bars, error_values):
                yval = bar.get_height()
                text_position = yval + error + 0.02
                plt.text(
                    bar.get_x() + bar.get_width() / 2,
                    text_position,
                    f"{yval:.2f}",
                    ha="center",
                    va="bottom",
                    fontsize=10,
                )

        plt.xlabel("Metrics", fontsize=14)
        plt.ylabel("Scores", fontsize=14)
        plt.title(
            f"Cross-Validation (k={visualizers[0].trainer.cv}) Evaluation Metrics Comparison",
            fontsize=16,
        )
        plt.xticks(index + bar_width * (len(model_names) - 1) / 2, metrics, fontsize=12)
        plt.ylim(0, 1.1)
        plt.legend(loc="upper left", bbox_to_anchor=(1, 1))

        plt.grid(True, which="major", linestyle="--", linewidth="0.5", color="grey")
        plt.tight_layout()
        plt.show()

    @staticmethod
    def compare_roc_curves(visualizers, dataset):
        if dataset not in ["test", "eval"]:
            raise ValueError("Invalid dataset option. Choose 'test' or 'eval'.")

        plt.figure(figsize=(8, 8))
        colors = plt.cm.viridis(np.linspace(0, 1, len(visualizers)))

        for i, viz in enumerate(visualizers):
            if dataset == "test":
                y_true = viz.trainer.data_module.y_test
                y_scores = viz.trainer.get_trained_model().predict_proba(
                    viz.trainer.data_module.X_test
                )[:, 1]
            elif dataset == "eval":
                y_true = []
                y_scores = []
                for fold in viz.trainer.get_train_metrics_report()["roc_data"]:
                    y_true.extend(fold[0])
                    y_scores.extend(fold[1][:, 1])

            fpr, tpr, _ = roc_curve(y_true, y_scores)
            auc_score = roc_auc_score(y_true, y_scores)
            plt.plot(
                fpr,
                tpr,
                label=f"{viz.model_name} (AUC = {auc_score:.2f})",
                color=colors[i],
            )

        plt.plot([0, 1], [0, 1], "k--")
        plt.xlabel("False Positive Rate")
        plt.ylabel("True Positive Rate")
        plt.title(f"ROC Curve Comparison on {dataset.capitalize()} Set")
        plt.legend(loc="lower right")
        plt.show()

    def plot_validation_metrics(self):
        train_metrics = self.trainer.get_train_metrics_report()
        cv = len(train_metrics["accuracy"]["folds"])

        metrics = self.trainer.get_scoring_metrics()
        fold_scores = {metric: train_metrics[metric]["folds"] for metric in metrics}

        plt.boxplot(fold_scores.values(), labels=metrics, notch=True)
        plt.title(f"{self.model_name}: Validation Metrics Box Plot (CV={cv})")
        plt.xlabel("Metrics")
        plt.ylabel("Score")
        plt.ylim(0, 1)
        plt.grid(True)
        plt.show()

    def plot_test_metrics(self):
        test_metrics = self.trainer.get_test_metrics_report()
        test_values = list(test_metrics.values())
        test_names = list(test_metrics.keys())

        sns.barplot(x=test_names, y=test_values)
        plt.title(f"{self.model_name}: Test Metrics")
        plt.xlabel("Metrics")
        plt.ylabel("Score")
        for i, v in enumerate(test_values):
            if np.isnan(v):
                plt.text(i, 0.5, "N/A", ha="center", va="bottom")
            else:
                plt.text(i, v + 0.01, f"{v:.2f}", ha="center", va="bottom")
        plt.ylim(0, 1)
        plt.grid(True)
        plt.show()

    def plot_confusion_matrix_test(self):
        preds = self.y_test_pred_proba.argmax(axis=1)
        skplt.metrics.plot_confusion_matrix(self.y_true, preds)
        plt.title(f"{self.model_name}: Confusion Matrix")
        plt.show()

    def plot_classification_report_test(self):
        preds = self.y_test_pred_proba.argmax(axis=1)
        report = classification_report(self.y_true, preds, output_dict=True)

        report_df = pd.DataFrame(report).transpose()
        report_df = report_df.round(2)

        table = plt.table(
            cellText=report_df.values,
            colLabels=report_df.columns,
            rowLabels=report_df.index,
            cellLoc="center",
            rowLoc="center",
            loc="center",
            fontsize=12,
        )
        table.auto_set_font_size(False)
        table.set_fontsize(12)
        table.scale(1.2, 1.2)

        plt.axis("off")
        plt.title(f"{self.model_name}: Classification Report")
        plt.show()

    def plot_threshold_optimization_test(self):
        precision, recall, thresholds = precision_recall_curve(
            self.y_true, self.y_test_pred_proba[:, 1]
        )
        f1_scores = 2 * (precision * recall) / (precision + recall)
        optimal_idx = np.argmax(f1_scores)
        optimal_threshold = thresholds[optimal_idx]

        plt.plot(thresholds, f1_scores[:-1], label="F1-score")
        plt.axvline(
            x=optimal_threshold,
            color="red",
            linestyle="--",
            label=f"Optimal Threshold: {optimal_threshold:.2f}",
        )
        plt.title(f"{self.model_name}: Threshold Optimization")
        plt.xlabel("Threshold")
        plt.ylabel("F1-score")
        plt.legend()
        plt.show()

    def plot_roc_curve_test(self):
        skplt.metrics.plot_roc(
            self.y_true, self.y_test_pred_proba, plot_micro=False, plot_macro=False
        )
        plt.title(f"{self.model_name}: ROC Curve on Test Set")
        plt.show()

    def plot_roc_curve_eval(self, show_folds=False):
        fig, ax = plt.subplots(figsize=(8, 8))
        colors = plt.cm.viridis(np.linspace(0, 1, self.trainer.cv))

        roc_data = self.trainer.get_train_metrics_report()["roc_data"]
        for k in range(self.trainer.cv):
            true_labels, y_pred_proba = roc_data[k]
            fpr, tpr, _ = roc_curve(true_labels, y_pred_proba[:, 1])
            auc_score = roc_auc_score(true_labels, y_pred_proba[:, 1])
            ax.plot(
                fpr, tpr, color=colors[k], label=f"Fold {k + 1} (AUC = {auc_score:.2f})"
            )

        plt.title(
            f"{self.model_name}: ROC Curves for each fold (CV={self.trainer.cv}, "
            f'Mean AUROC={self.trainer.train_metrics_report["roc_auc"]["mean"]:.2f})'
        )
        if show_folds:
            plt.legend(loc="lower right")

        plt.xlabel("False Positive Rate")
        plt.ylabel("True Positive Rate")
        plt.grid(True)
        plt.show()

    def plot_precision_recall_curve_test(self):
        skplt.metrics.plot_precision_recall(self.y_true, self.y_test_pred_proba)
        plt.title(f"{self.model_name}: Precision-Recall Curve on Test Set")
        plt.show()

    def plot_lift_curve_test(self):
        skplt.metrics.plot_lift_curve(self.y_true, self.y_test_pred_proba)
        plt.title(f"{self.model_name}: Lift Curve on Test Set")
        plt.show()

    def plot_cumulative_gain_curve_test(self):
        skplt.metrics.plot_cumulative_gain(self.y_true, self.y_test_pred_proba)
        plt.title(f"{self.model_name}: Cumulative Gain Curve on Test Set")
        plt.show()

    def plot_partial_dependence_test(self, feature):
        pdp = self.explainer.model_profile(type="partial", variables=feature)
        pdp.plot()

    def plot_accumulated_local_effects_test(self, feature):
        ale = self.explainer.model_profile(type="accumulated", variables=feature)
        ale.plot()

    def plot_breakdown_test(self, observation):
        breakdown = self.explainer.predict_parts(observation, type="break_down")
        breakdown.plot()

    def plot_model_explanations_test(self):
        feature_importance = self.explainer.model_parts()
        feature_importance.plot()

        # model_profile = self.explainer.model_profile(type="partial")
        # model_profile.plot()

    def plot_grid_search(self, log_scale_params):
        if self.trainer.param_grid is None:
            raise ValueError("No hyperparameter grid was provided during model training.")
        
        cv_results = pd.DataFrame(self.trainer.get_pipeline().named_steps["model_pipeline"].cv_results_)
        
        def shorten_param(param_name):
            if "__" in param_name:
                return param_name.rsplit("__", 1)[1]
            return param_name
        
        cv_results = cv_results.rename(shorten_param, axis=1)
        
        params = {}
        for param in log_scale_params:
            if cv_results[param].dtype in ["int64", "float64"]:
                params[param] = lambda x: np.log10(x) if x > 0 else 0
            else:
                params[param] = lambda x: x
        
        fig = px.parallel_coordinates(
            cv_results.apply(
                {
                    **params,
                    'mean_test_score': lambda x: x
                }
            ),
            color="mean_test_score",
            color_continuous_scale=px.colors.sequential.Viridis
        )
        fig.show()

    def visualize_explanations_test(self, feature_columns=[]):
        self.plot_model_explanations()

        if not feature_columns:
            feature_columns = self.trainer.data_module.feature_columns[0]

        self.plot_partial_dependence(feature_columns)
        self.plot_accumulated_local_effects(feature_columns)

        observation = self.trainer.data_module.X_test.iloc[0]
        self.plot_breakdown(observation)

        plt.show()

11.2 Baseline Model: Logistic Regression

baseline_feature_columns = ["age", "client_region"] + [
    col
    for col in golden_record_df.columns
    if "M_" in col and ("_balance" in col or "_volume" in col)
]

baseline_data_module = create_data_module(golden_record_df, baseline_feature_columns)

print(f"Number of baseline feature columns: {len(baseline_feature_columns)}")
print(f"Baseline feature columns: {baseline_feature_columns}")

Number of baseline feature columns: 26
Baseline feature columns: ['age', 'client_region', 'M_2_volume', 'M_3_volume', 'M_4_volume', 'M_5_volume', 'M_6_volume', 'M_7_volume', 'M_8_volume', 'M_9_volume', 'M_10_volume', 'M_11_volume', 'M_12_volume', 'M_13_volume', 'M_2_balance', 'M_3_balance', 'M_4_balance', 'M_5_balance', 'M_6_balance', 'M_7_balance', 'M_8_balance', 'M_9_balance', 'M_10_balance', 'M_11_balance', 'M_12_balance', 'M_13_balance']

from sklearn.linear_model import LogisticRegression

baseline_trainer = (
    Trainer(baseline_data_module, LogisticRegression(max_iter=10000)).fit().eval_train()
)

baseline_visualizer = Visualizer(baseline_trainer, "Baseline Logistic Regression")
baseline_visualizer.plot_validation_metrics()

Preparation of a new explainer is initiated

  -> data              : 132 rows 26 cols
  -> target variable   : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
  -> target variable   : 132 values
  -> model_class       : sklearn.pipeline.Pipeline (default)
  -> label             : Not specified, model's class short name will be used. (default)
  -> predict function  : <function yhat_proba_default at 0x7fed5ecf8360> will be used (default)
  -> predict function  : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
  -> predicted values  : min = 0.0111, mean = 0.518, max = 1.0
  -> model type        : classification will be used (default)
  -> residual function : difference between y and yhat (default)
  -> residuals         : min = -0.971, mean = -0.0183, max = 0.803
  -> model_info        : package sklearn

A new explainer has been created!

baseline_visualizer.plot_confusion_matrix_test()

baseline_visualizer.plot_roc_curve_eval(show_folds=True)

11.3 Adding more features

In order to possibly improve the model performance, we will include more features in the training data. We will include all features except for the ones that are not relevant for the model training.

After merging the transactional and non-transactional data, we have many columns that are unnecessary for model training. We will remove all columns containing card-related information, except for the has_card column. This decision stems from the fact that 50% of our dataset consists of cardholders and the other 50% consists of non-cardholders, which we matched with the cardholders. Therefore, the data in the non-target card-related columns come from the actual cardholders.

Additionally we will remove all columns that contain time-dependent information, such as dates and IDs, as they are not relevant for the model.

num_cols_before = len(golden_record_df.columns)
golden_record_df = golden_record_df.loc[
    :,
    ~golden_record_df.columns.str.contains("card")
    | golden_record_df.columns.str.contains("has_card"),
]
print(
    f"Removed {num_cols_before - len(golden_record_df.columns)} card-related columns. Now {len(golden_record_df.columns)} columns remain."
)

num_cols_before = len(golden_record_df.columns)
golden_record_df = golden_record_df.drop(
    columns=["loan_granted_date", "birth_date", "account_created"]
)
print(
    f"Removed {num_cols_before - len(golden_record_df.columns)} time-dependent columns. Now {len(golden_record_df.columns)} columns remain."
)

num_cols_before = len(golden_record_df.columns)
golden_record_df = golden_record_df.drop(
    columns=[
        "loan_account_id",
        "loan_loan_id",
        "order_account_id",
        "client_district_name",
        "disp_id",
        "account_id",
        "account_district_name",
    ]
)
print(
    f"Removed {num_cols_before - len(golden_record_df.columns)} ID columns. Now {len(golden_record_df.columns)} columns remain."
)

num_cols_before = len(golden_record_df.columns)
golden_record_df = golden_record_df.drop(
    columns=[col for col in golden_record_df.columns if "std" in col]
)
print(
    f"Removed {num_cols_before - len(golden_record_df.columns)} std columns. Now {len(golden_record_df.columns)} columns remain."
)

cols_to_exclude_in_train = ["client_id", "has_card"]
all_cols_data_module = create_data_module(
    golden_record_df, golden_record_df.drop(columns=cols_to_exclude_in_train).columns
)

Removed 6 card-related columns. Now 226 columns remain.
Removed 3 time-dependent columns. Now 223 columns remain.
Removed 7 ID columns. Now 216 columns remain.
Removed 12 std columns. Now 204 columns remain.

11.4 Candidate Models

11.4.1 Logistic Regression

We will train a logistic regression model with the new feature set and evaluate its performance as it already showed promising results in the baseline model.

log_reg_trainer = (
    Trainer(all_cols_data_module, LogisticRegression(max_iter=10000)).fit().eval_train()
)

log_reg_visualizer = Visualizer(log_reg_trainer, "Logistic Regression")
log_reg_visualizer.plot_validation_metrics()

Preparation of a new explainer is initiated

  -> data              : 132 rows 202 cols
  -> target variable   : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
  -> target variable   : 132 values
  -> model_class       : sklearn.pipeline.Pipeline (default)
  -> label             : Not specified, model's class short name will be used. (default)
  -> predict function  : <function yhat_proba_default at 0x7fed5ecf8360> will be used (default)
  -> predict function  : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
  -> predicted values  : min = 8.33e-06, mean = 0.477, max = 1.0
  -> model type        : classification will be used (default)
  -> residual function : difference between y and yhat (default)
  -> residuals         : min = -1.0, mean = 0.0227, max = 0.98
  -> model_info        : package sklearn

A new explainer has been created!

log_reg_visualizer.plot_confusion_matrix_test()

log_reg_visualizer.plot_roc_curve_eval(show_folds=True)

11.4.2 Random Forest

We will also train a Random Forest model to see if it can outperform the logistic regression model. Random Forest models are known for their robustness and ability to capture complex relationships in the data.

from sklearn.ensemble import RandomForestClassifier

rf_trainer = (
    Trainer(
        all_cols_data_module,
        RandomForestClassifier(),
    )
    .fit()
    .eval_train()
)

rf_visualizer = Visualizer(rf_trainer, "Random Forest")
rf_visualizer.plot_validation_metrics()

Preparation of a new explainer is initiated

  -> data              : 132 rows 202 cols
  -> target variable   : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
  -> target variable   : 132 values
  -> model_class       : sklearn.pipeline.Pipeline (default)
  -> label             : Not specified, model's class short name will be used. (default)
  -> predict function  : <function yhat_proba_default at 0x7fed5ecf8360> will be used (default)
  -> predict function  : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
  -> predicted values  : min = 0.0, mean = 0.49, max = 0.99
  -> model type        : classification will be used (default)
  -> residual function : difference between y and yhat (default)
  -> residuals         : min = -0.86, mean = 0.00977, max = 0.74
  -> model_info        : package sklearn

A new explainer has been created!

rf_visualizer.plot_confusion_matrix_test()

rf_visualizer.plot_roc_curve_eval(show_folds=True)

11.4.3 Decision Tree

We will also train a Decision Tree model to see how it performs compared to the other models. Decision Trees are known for their interpretability and simplicity.

from sklearn.tree import DecisionTreeClassifier

decision_tree_trainer = (
    Trainer(
        all_cols_data_module,
        DecisionTreeClassifier(),
    )
    .fit()
    .eval_train()
)

decision_tree_visualizer = Visualizer(decision_tree_trainer, "Decision Tree")
decision_tree_visualizer.plot_validation_metrics()

Preparation of a new explainer is initiated

  -> data              : 132 rows 202 cols
  -> target variable   : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
  -> target variable   : 132 values
  -> model_class       : sklearn.pipeline.Pipeline (default)
  -> label             : Not specified, model's class short name will be used. (default)
  -> predict function  : <function yhat_proba_default at 0x7fed5ecf8360> will be used (default)
  -> predict function  : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
  -> predicted values  : min = 0.0, mean = 0.477, max = 1.0
  -> model type        : classification will be used (default)
  -> residual function : difference between y and yhat (default)
  -> residuals         : min = -1.0, mean = 0.0227, max = 1.0
  -> model_info        : package sklearn

A new explainer has been created!

decision_tree_visualizer.plot_confusion_matrix_test()

decision_tree_visualizer.plot_roc_curve_eval(show_folds=True)

11.4.4 Gradient Boosting

Finally, we will train a Gradient Boosting model to see if it can outperform the other models. Gradient Boosting models are known for their high accuracy and ability to capture complex relationships in the data.

from sklearn.ensemble import GradientBoostingClassifier

gradient_boost_trainer = (
    Trainer(
        all_cols_data_module,
        GradientBoostingClassifier(),
    )
    .fit()
    .eval_train()
)

gradient_boost_visualizer = Visualizer(gradient_boost_trainer, "Gradient Boosting")
gradient_boost_visualizer.plot_validation_metrics()

Preparation of a new explainer is initiated

  -> data              : 132 rows 202 cols
  -> target variable   : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
  -> target variable   : 132 values
  -> model_class       : sklearn.pipeline.Pipeline (default)
  -> label             : Not specified, model's class short name will be used. (default)
  -> predict function  : <function yhat_proba_default at 0x7fed5ecf8360> will be used (default)
  -> predict function  : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
  -> predicted values  : min = 0.00414, mean = 0.508, max = 0.985
  -> model type        : classification will be used (default)
  -> residual function : difference between y and yhat (default)
  -> residuals         : min = -0.985, mean = -0.00813, max = 0.909
  -> model_info        : package sklearn

A new explainer has been created!

gradient_boost_visualizer.plot_confusion_matrix_test()

gradient_boost_visualizer.plot_roc_curve_eval(show_folds=True)

12 Model Comparison & Selection

candidate_trainers = [
    baseline_trainer,
    log_reg_trainer,
    rf_trainer,
    decision_tree_trainer,
    gradient_boost_trainer,
]
candidate_visualizers = [
    baseline_visualizer,
    log_reg_visualizer,
    rf_visualizer,
    decision_tree_visualizer,
    gradient_boost_visualizer,
]

Visualizer.compare_evaluation_metrics(candidate_visualizers)

Visualizer.compare_roc_curves(candidate_visualizers, dataset="eval")

12.1 Top-N Customer Selection

We will now use the trained models to generate a list of the top N customers who are most likely to get a card (according to the model). We will compare the lists generated by each model to see if there is any overlap in the predictions.

def create_top_n_customers_list(model, data):
    mandatory_columns = ["client_id", "has_card"]

    if not hasattr(model, "predict_proba"):
        raise ValueError("Model does not support probability predictions")

    if not all(col in data.columns for col in mandatory_columns):
        raise ValueError("Mandatory columns not found in data: 'client_id', 'has_card'")

    data = data[data["has_card"] == 0]

    probabilities = model.predict_proba(data.copy())
    # Probability of having a card (class 1). This essentially gives the clients who should most likely have a card based on the model but don't have one.
    probabilities = probabilities[:, 1]

    results = pd.DataFrame(
        {"Client ID": data["client_id"], "Probability": probabilities}
    )

    return results.sort_values(by="Probability", ascending=False).reset_index(drop=True)


def compare_top_n_lists(*lists, labels, top_n_percent):
    if len(lists) != len(labels):
        raise ValueError("Each list must have a corresponding label")

    if len(set([len(l) for l in lists])) != 1:
        raise ValueError("All lists must have the same length")

    for l in lists:
        if not l["Probability"].is_monotonic_decreasing:
            raise ValueError("Lists must be sorted in descending order of probability")

    top_n = int(len(lists[0]) * top_n_percent)
    lists = [l.head(top_n) for l in lists]

    overlap_matrix = pd.DataFrame(0, index=labels, columns=labels)

    for i, list1 in enumerate(lists):
        set1 = set(list1["Client ID"])
        for j, list2 in enumerate(lists):
            set2 = set(list2["Client ID"])
            overlap_matrix.iloc[i, j] = len(set1.intersection(set2))

    overlap_matrix = overlap_matrix / len(lists[0])
    return overlap_matrix


def visualize_overlap_matrix(overlap_matrix, title):
    plt.figure(figsize=(10, 8))

    mask = np.tril(np.ones_like(overlap_matrix, dtype=bool))
    overlap_matrix = overlap_matrix.mask(mask)

    sns.heatmap(
        overlap_matrix,
        annot=True,
        cmap="Blues",
        cbar_kws={"label": "Common Customers [%]"},
    )
    plt.title(title)
    plt.ylabel("List from Model/Method")
    plt.xlabel("List from Model/Method")
    plt.xticks(
        ticks=np.arange(len(overlap_matrix.columns)) + 0.5,
        labels=overlap_matrix.columns,
        rotation=45,
        ha="right",
    )
    plt.yticks(
        ticks=np.arange(len(overlap_matrix.index)) + 0.5,
        labels=overlap_matrix.index,
        rotation=0,
    )
    plt.show()

12.2 Selected Model: Random Forest

best_model_trainer = rf_trainer
best_model_visualizer = rf_visualizer

best_model_trainer.eval_test()

<__main__.Trainer at 0x7fed5edecb90>

best_model_visualizer.plot_test_metrics()

best_model_visualizer.plot_roc_curve_test()

_, _ = (
    best_model_visualizer.plot_confusion_matrix_test(),
    best_model_visualizer.plot_classification_report_test(),
)

13 Model Optimization

After selecting the best model, we can further optimize its hyperparameters to improve its performance. We will use grid search with cross-validation to find the best hyperparameters for the logistic regression model.

gs_param_grid = {
    "model__n_estimators": [50, 100, 200],
    "model__max_depth": [5, 10, 20],
    "model__min_samples_split": [2, 5, 10],
    "model__min_samples_leaf": [1, 2, 4],
}

gs_trainer = (
    Trainer(
        all_cols_data_module,
        RandomForestClassifier(),
        param_grid=gs_param_grid,
        verbose=True,
    )
    .fit()
    .eval_train()
)

gs_trainer.get_best_params()

Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.868 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.808 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time=   0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.731 total time=   0.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.788 total time=   0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.811 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.731 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.808 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.788 total time=   0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.769 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.788 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time=   0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time=   0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.887 total time=   0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.769 total time=   0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.885 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.788 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.811 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.885 total time=   0.6s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.788 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time=   0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.788 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.811 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.811 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.808 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time=   0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time=   0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.865 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.887 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.811 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.887 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.811 total time=   0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.811 total time=   0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time=   0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.750 total time=   0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time=   0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.906 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.769 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.750 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.750 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.887 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.769 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.811 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.788 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.868 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.925 total time=   0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time=   0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.750 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.769 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.788 total time=   0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.788 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.808 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.788 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time=   0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.887 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.887 total time=   0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.865 total time=   0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.769 total time=   0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.788 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.868 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.769 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.904 total time=   0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.788 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.811 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.769 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.811 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.887 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.808 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time=   0.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time=   0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time=   0.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.811 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.887 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time=   0.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time=   0.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time=   0.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.769 total time=   0.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.904 total time=   0.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.769 total time=   0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.811 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.808 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.887 total time=   0.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.769 total time=   0.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.885 total time=   0.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.788 total time=   0.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.788 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.865 total time=   0.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.769 total time=   0.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time=   0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.811 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.808 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.811 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.788 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.906 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.808 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time=   0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.811 total time=   0.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.750 total time=   0.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.885 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.769 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.811 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time=   0.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time=   0.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.788 total time=   0.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time=   0.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.865 total time=   0.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.788 total time=   0.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.885 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.887 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.808 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.904 total time=   0.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.750 total time=   0.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.769 total time=   0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.887 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.885 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.788 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time=   0.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.788 total time=   0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.887 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.788 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time=   0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.769 total time=   0.7s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.904 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.808 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time=   0.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time=   0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.808 total time=   0.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.750 total time=   0.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.769 total time=   0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.811 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.887 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.788 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.885 total time=   0.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.731 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.808 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time=   0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time=   0.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.769 total time=   0.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.887 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.788 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.811 total time=   0.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.885 total time=   0.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.750 total time=   0.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.904 total time=   0.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.769 total time=   0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.811 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.885 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.887 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.811 total time=   0.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.769 total time=   0.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time=   0.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.769 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time=   0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time=   0.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time=   0.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time=   0.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.788 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.731 total time=   0.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.788 total time=   0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.811 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.885 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time=   0.7s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.811 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.811 total time=   0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.868 total time=   0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.849 total time=   0.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.868 total time=   0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.731 total time=   0.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.865 total time=   0.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.731 total time=   0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.788 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.788 total time=   0.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time=   0.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time=   0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.887 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.7s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.887 total time=   0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time=   0.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time=   0.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.750 total time=   0.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.769 total time=   0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.887 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.811 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.849 total time=   0.7s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.885 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.769 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.788 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.808 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.811 total time=   0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time=   0.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time=   0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time=   0.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time=   0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.849 total time=   0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.811 total time=   0.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time=   0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time=   0.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.788 total time=   0.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.769 total time=   0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.808 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.808 total time=   0.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time=   0.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.811 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.811 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.811 total time=   0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.868 total time=   0.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.868 total time=   0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time=   0.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.731 total time=   0.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.865 total time=   0.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.788 total time=   0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.811 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.904 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.769 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.849 total time=   0.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.865 total time=   0.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.769 total time=   0.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time=   0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.4s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.849 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.808 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.885 total time=   0.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.750 total time=   0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.811 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.788 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time=   0.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.811 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.885 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.808 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.827 total time=   0.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.887 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.808 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.887 total time=   0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.865 total time=   0.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.750 total time=   0.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.868 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.865 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.788 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.887 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.827 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.811 total time=   0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.868 total time=   0.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.827 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.769 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.731 total time=   0.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.904 total time=   0.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.808 total time=   0.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.808 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.750 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.865 total time=   0.6s
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.868 total time=   0.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.827 total time=   0.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.846 total time=   0.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.849 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.731 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.846 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.788 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.849 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.868 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.846 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.769 total time=   0.5s
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.792 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.723 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.936 total time=   1.0s[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s

[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.723 total time=   0.6s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.745 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.915 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.723 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.745 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.723 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.792 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.915 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.771 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.723 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.745 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.915 total time=   0.7s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.745 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.792 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.936 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.771 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.723 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.792 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.745 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.936 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.915 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.792 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.771 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.745 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.915 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.936 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.7s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.915 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.915 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.7s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.750 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.792 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.792 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.7s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.957 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.723 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV] END  accuracy: (test=0.830) f1_macro: (test=0.830) precision: (test=0.815) recall: (test=0.846) roc_auc: (test=0.873) total time= 4.4min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.745 total time=   1.1s
[CV] END  accuracy: (test=0.830) f1_macro: (test=0.830) precision: (test=0.815) recall: (test=0.846) roc_auc: (test=0.893) total time= 4.4min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.745 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.745 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.915 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.915 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.723 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.7s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.7s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.792 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.7s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.745 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.723 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.771 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.792 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.771 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.915 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.771 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.792 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.745 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.745 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.792 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.4s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.4s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.936 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.792 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV] END  accuracy: (test=0.830) f1_macro: (test=0.852) precision: (test=0.765) recall: (test=0.963) roc_auc: (test=0.858) total time= 4.4min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV] END  accuracy: (test=0.868) f1_macro: (test=0.873) precision: (test=0.857) recall: (test=0.889) roc_auc: (test=0.912) total time= 4.4min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time=   1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.792 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.745 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.917 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.896 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.896 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.896 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.745 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.7s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.792 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.896 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.771 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.896 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.896 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.745 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.7s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.915 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.745 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.4s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.771 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.745 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.792 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.745 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.771 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.896 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV] END  accuracy: (test=0.846) f1_macro: (test=0.867) precision: (test=0.765) recall: (test=1.000) roc_auc: (test=0.913) total time= 4.3min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.896 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV] END  accuracy: (test=0.750) f1_macro: (test=0.787) precision: (test=0.686) recall: (test=0.923) roc_auc: (test=0.845) total time= 4.4min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.915 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.896 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.771 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.896 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.896 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.896 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.896 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.896 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.771 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.896 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.915 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.771 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.745 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.792 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.896 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.896 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.896 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.896 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.771 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.896 total time=   0.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.745 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.771 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.792 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.896 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.4s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.8s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.7s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.915 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.745 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.896 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.771 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   0.9s
[CV] END  accuracy: (test=0.827) f1_macro: (test=0.830) precision: (test=0.815) recall: (test=0.846) roc_auc: (test=0.882) total time= 4.3min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV] END  accuracy: (test=0.827) f1_macro: (test=0.842) precision: (test=0.774) recall: (test=0.923) roc_auc: (test=0.913) total time= 4.4min
Fitting 10 folds for each of 81 candidates, totalling 810 fits
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.750 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.896 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.896 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.957 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.2s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.957 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.936 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.1s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.792 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.915 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.936 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.915 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.2s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.2s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.2s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.896 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.2s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.2s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 1/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.0s
[CV 2/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.0s
[CV 3/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 5/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 6/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.0s
[CV 8/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 9/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.0s
[CV 10/10] END model__max_depth=5, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.0s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.936 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.4s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.4s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.792 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.915 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.915 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.766 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.745 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.936 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.915 total time=   1.2s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.792 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.771 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.936 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.936 total time=   0.6s
[CV 1/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.0s
[CV 4/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 9/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 10/10] END model__max_depth=10, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.936 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200;, score=0.915 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.812 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.792 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.766 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=100;, score=0.915 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=1, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.2s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.2s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.2s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.766 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=50;, score=0.915 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100;, score=0.894 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200;, score=0.915 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.833 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.812 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.2s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.2s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=2, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.2s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.875 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=50;, score=0.894 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.854 total time=   0.5s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=100;, score=0.915 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.875 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=2, model__n_estimators=200;, score=0.915 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.896 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.896 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.833 total time=   0.5s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.851 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.915 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.872 total time=   1.1s

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed: 21.8min finished

{'max_depth': 10,
 'min_samples_leaf': 4,
 'min_samples_split': 2,
 'n_estimators': 100}

gs_visualizer = Visualizer(gs_trainer, "Random Forest Grid Search")
gs_visualizer.plot_grid_search(log_scale_params=["n_estimators", "max_depth", "min_samples_split", "min_samples_leaf"])

Preparation of a new explainer is initiated

  -> data              : 132 rows 202 cols
  -> target variable   : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
  -> target variable   : 132 values
  -> model_class       : sklearn.model_selection._search.GridSearchCV (default)
  -> label             : Not specified, model's class short name will be used. (default)
  -> predict function  : <function yhat_proba_default at 0x7fed5ecf8360> will be used (default)
  -> predict function  : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
  -> predicted values  : min = 0.0, mean = 0.494, max = 0.966
  -> model type        : classification will be used (default)
  -> residual function : difference between y and yhat (default)
  -> residuals         : min = -0.837, mean = 0.00555, max = 0.701
  -> model_info        : package sklearn

A new explainer has been created!

gs_visualizer.plot_validation_metrics()

gs_visualizer.plot_roc_curve_eval(show_folds=True)

gs_visualizer.plot_lift_curve_test()

gs_trainer.eval_test()
gs_visualizer.plot_test_metrics()

_, _ = (
    gs_visualizer.plot_confusion_matrix_test(),
    gs_visualizer.plot_classification_report_test()
)

Visualizer.compare_evaluation_metrics([best_model_visualizer, gs_visualizer])

14 Model Explanation & Reduction

reduced_best_model_trainer = (
    Trainer(
        all_cols_data_module,
        RandomForestClassifier(**gs_trainer.get_best_params()),
        select_features=True,
    )
    .fit()
    .eval_train()
)

reduced_best_model_trainer.get_selected_features()

[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.830 total time=   0.6s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.833 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.830 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.896 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.5s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.830 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.766 total time=   0.5s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.0s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   1.0s
[CV] END  accuracy: (test=0.865) f1_macro: (test=0.863) precision: (test=0.880) recall: (test=0.846) roc_auc: (test=0.935) total time= 4.4min
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.787 total time=   1.1s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.851 total time=   1.1s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=5, model__n_estimators=200;, score=0.894 total time=   1.1s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.812 total time=   0.3s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.854 total time=   0.3s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.766 total time=   0.3s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.787 total time=   0.3s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.872 total time=   0.3s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.809 total time=   0.3s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=50;, score=0.851 total time=   0.3s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.875 total time=   0.6s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.854 total time=   0.6s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.872 total time=   0.6s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.787 total time=   0.6s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.851 total time=   0.5s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.809 total time=   0.5s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=100;, score=0.936 total time=   0.6s
[CV 1/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.833 total time=   1.1s
[CV 2/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.854 total time=   1.1s
[CV 3/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 4/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.830 total time=   1.1s
[CV 5/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.872 total time=   1.1s
[CV 6/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   1.1s
[CV 7/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.787 total time=   0.9s
[CV 8/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.851 total time=   0.9s
[CV 9/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.809 total time=   0.7s
[CV 10/10] END model__max_depth=20, model__min_samples_leaf=4, model__min_samples_split=10, model__n_estimators=200;, score=0.894 total time=   0.5s
[CV] END  accuracy: (test=0.788) f1_macro: (test=0.820) precision: (test=0.714) recall: (test=0.962) roc_auc: (test=0.892) total time= 4.3min

['age',
 'client_tenure_years_relative',
 'M_3_volume',
 'M_4_volume',
 'M_7_volume',
 'M_12_volume',
 'M_4_average_amount',
 'M_12_average_amount',
 'M_11_median_amount',
 'M_2_min_amount',
 'M_3_min_amount',
 'M_12_min_amount',
 'M_2_max_amount',
 'M_3_max_amount',
 'M_4_max_amount',
 'M_8_max_amount',
 'M_10_max_amount',
 'M_2_balance',
 'M_3_balance',
 'M_4_balance',
 'M_5_balance',
 'M_6_balance',
 'M_7_balance',
 'M_8_balance',
 'M_9_balance',
 'M_10_balance',
 'M_11_balance',
 'M_12_balance',
 'M_13_balance']

reduced_best_model_visualizer = Visualizer(
    reduced_best_model_trainer, "Reduced Random Forest"
)

reduced_best_model_visualizer.plot_validation_metrics()

Preparation of a new explainer is initiated

  -> data              : 132 rows 202 cols
  -> target variable   : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.
  -> target variable   : 132 values
  -> model_class       : sklearn.pipeline.Pipeline (default)
  -> label             : Not specified, model's class short name will be used. (default)
  -> predict function  : <function yhat_proba_default at 0x7fed5ecf8360> will be used (default)
  -> predict function  : Accepts only pandas.DataFrame, numpy.ndarray causes problems.
  -> predicted values  : min = 0.0, mean = 0.492, max = 0.983
  -> model type        : classification will be used (default)
  -> residual function : difference between y and yhat (default)
  -> residuals         : min = -0.899, mean = 0.00817, max = 0.779
  -> model_info        : package sklearn

A new explainer has been created!

Visualizer.compare_evaluation_metrics(
    [best_model_visualizer, gs_visualizer, reduced_best_model_visualizer]
)

reduced_best_model_visualizer.plot_confusion_matrix_test()

# reduced_best_model_visualizer.plot_grid_search(log_scale_params=["n_estimators", "max_depth", "min_samples_split", "min_samples_leaf"])

reduced_best_model_visualizer.plot_model_explanations_test()

14.1 Lift Curve

reduced_best_model_visualizer.plot_lift_curve_test()

14.1.1 Top-10% Customer Selection

We will select the top 10% of customers who are most likely to get a card according to each model.

candidate_trainers.append(reduced_best_model_trainer)

customer_lists = [
    create_top_n_customers_list(trainer.get_pipeline(), golden_record_df)
    for trainer in candidate_trainers
]
candidate_labels = [
    "Baseline",
    "Logistic Regression",
    "Random Forest",
    "Decision Tree",
    "Gradient Boosting",
    "Random Forest (Optimized)"
]

top_10_overlap_matrix = compare_top_n_lists(
    *customer_lists, labels=candidate_labels, top_n_percent=0.1
)
visualize_overlap_matrix(
    top_10_overlap_matrix, "Overlap of Top-10% Customer Lists by Model"
)

14.1.2 Top-5% Customer Selection

We will select the top 5% of customers who are most likely to get a card according to each model.

top_5_overlap_matrix = compare_top_n_lists(
    *customer_lists, labels=candidate_labels, top_n_percent=0.05
)
visualize_overlap_matrix(
    top_5_overlap_matrix, "Overlap of Top-5% Customer Lists by Model"
)

15 Conclusion